DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement filed 14 July 2025 fails to comply with the provisions of 37 CFR 1.98(a)(4) because it lacks the appropriate size fee assertion. It has been placed in the application file, but the information referred to therein has not been considered as to the merits.
Drawings
The drawings are objected to because the unlabeled rectangular boxes shown in the drawings should be provided with descriptive text labels, see figures 1 and 12 - 15. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 disclosure is objected to because of the following informalities: Line 2 of paragraph 0013 on page 4 of the instant specification recites, in part, “provided as hardware, they functions of one or more modules” which appears to contain a typographical error and/or a minor informality. The Examiner suggests amending the disclosure to --provided as hardware, the functions of one or more modules-- in order to improve the clarity and precision of the disclosure. Appropriate correction is required.
The disclosure is objected to because of the following informalities: Lines 2 - 3 of paragraph 0070 on page 19 of the instant specification recite, in part, “and the horizontal axis shows a variable based on the number of clusters” which appears to contain a typographical error and/or a minor informality. The Examiner suggests amending the disclosure to --and the vertical axis shows a variable based on the number of clusters-- in order to improve the clarity and precision of the disclosure. Appropriate correction is required.
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
Claim Objections
Claim 1 is objected to because of the following informalities: Line 6 of claim 1 recites “as an image embedding for each image;” which appears to contain a minor informality. The Examiner suggests amending the claim to --as an image embedding for each image of the plurality of images;-- in order to improve the clarity and precision of the claim. Appropriate correction is required.
Claim 3 is objected to because of the following informalities: Line 3 of claim 3 recites, in part, “curve therein, and wherein the measure is an area” which appears to contain a grammatical error and/or minor informality. The Examiner suggests amending the claim to --curve therein, and. Appropriate correction is required.
Claim 5 is objected to because of the following informalities: Lines 7 - 8 of claim 5 recite, in part, “the evaluation module based on the adapted set of images, lies within a desired tolerance interval” which appears to contain a grammatical error and/or minor informality. The Examiner suggests amending the claim to --the evaluation module based on the adapted set of images[[,]] lies within a desired tolerance interval-- in order to improve the clarity and precision of the claim. Appropriate correction is required.
Claim 12 is objected to because of the following informalities: Lines 6 - 7 of claim 12 recite, in part, “evaluation module based on the adapted set of images, lies within a desired tolerance interval” which appears to contain a grammatical error and/or minor informality. The Examiner suggests amending the claim to --evaluation module based on the adapted set of images[[,]] lies within a desired tolerance interval-- in order to improve the clarity and precision of the claim. Appropriate correction is required.
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: “an input interface configured to receive”, “an image embeddings generating module configured to receive”, “a clustering module configured to determine”, "an evaluation module configured to construct”, “a user interface configured to receive”, “a data adaptation module configured to obtain”, “a machine learning module and is configured to eliminate”, “a training module configured to use” and “a visualization module configured to perform” in claims 1 and 4 - 10.
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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 - 15 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 1 recites the limitation "the number of clusters within the set of clusters determined by the clustering module when using a respective clustering parameter value of the plurality of clustering parameter values;" (emphasis added) in lines 14 - 17. There is insufficient antecedent basis for this limitation in the claim.
Claim 4 recites the limitation "the addition or removal of at least one image" in lines 2 - 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "the desired value of the measure" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "the desired tolerance interval" in line 4. There is insufficient antecedent basis for this limitation in the claim.
Claim 11 recites the limitation "the number of clusters determined using a respective clustering parameter value of the plurality of clustering parameter values;" (emphasis added) in lines 14 - 16. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation "the evaluation module" in lines 5 - 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 13 recites the limitation "the image embeddings generating module" in line 3. There is insufficient antecedent basis for this limitation in the claim.
Claims 2, 3, 5, 7 - 10, 14 and 15 are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, due to being dependent upon a rejected base claim(s) but would be withdrawn from the rejection if their base claim(s) overcome the rejection.
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.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the claim is directed towards a “computer program product comprising executable program code configured to, when executed, perform…” which is neither a process, a machine, manufacture, nor composition of matter. The Examiner suggests amending the claim so that it is directed towards one of the four categories of patent eligible subject matter, for example, “A non-transitory computer-readable storage medium
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (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 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 - 4, 6, 10, 11, 13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Davidson et al. U.S. Publication No. 2021/0081822 A1 in view of Ghulam Jilani Quadri, Jennifer Adorno Nieves, Brenton M. Wiernik and Paul Rosen, “Automatic Scatterplot Design Optimization for Clustering Identification”, arXiv, arXiv:2207.03355v1, 7 Jul. 2022, pages 1 -16, herein referred to as “Quadri et al.”.
- With regards to claims 1 and 11, Davidson et al. disclose a computing device (Davidson et al., Abstract, Fig. 8, Pg. 1 ¶ 0005 and 0016, Pg. 2 ¶ 0021 - 0023, Pg. 4 ¶ 0036 - 0038, Pg. 5 ¶ 0047, Pg. 7 ¶ 0061 - Pg. 8 ¶ 0064) and a computer-implemented method for preparing training data, (Davidson et al., Abstract, Figs. 1, 7 & 8, Pg. 1 ¶ 0004 - 0006 and 0016, Pg. 2 ¶ 0021 - 0025, Pg. 3 ¶ 0031 - 0032, Pg. 4 ¶ 0036 - 0038, Pg. 5 ¶ 0047, Pg. 7 ¶ 0060 - Pg. 8 ¶ 0064) comprising: an input interface (Davidson et al., Abstract, Figs. 1, 3 & 6 - 8, Pg. 1 ¶ 0005 - 0006, Pg. 2 ¶ 0022 - 0025, Pg. 4 ¶ 0037 - 0038, Pg. 7 ¶ 0059 - Pg. 8 ¶ 0064) configured to receive a plurality of images of a medical scene; (Davidson et al., Pg. 2 ¶ 0024 - 0025, Pg. 4 ¶ 0038, Pg. 6 ¶ 0053) an image embeddings generating module (Davidson et al., Figs. 1, 3 & 8, Pg. 1 ¶ 0005, Pg. 2 ¶ 0021 - 0023, Pg. 2 ¶ 0026 - Pg. 3 ¶ 0028, Pg. 4 ¶ 0036 - 0037 and 0039, Pg. 7 ¶ 0059, Pg. 7 ¶ 0061 - Pg. 8 ¶ 0064) configured to receive, as its input, the plurality of images and to generate a data array as an image embedding for each image; (Davidson et al., Figs. 1 & 3, Pg. 1 ¶ 0005, Pg. 2 ¶ 0026 - Pg. 3 ¶ 0028, Pg. 4 ¶ 0036 and 0039, Pg. 7 ¶ 0059) a clustering module (Davidson et al., Abstract, Figs. 1, 3, 6 & 8, Pg. 1 ¶ 0005 and 0016, Pg. 2 ¶ 0021 - 0023, Pg. 3 ¶ 0029, Pg. 5 ¶ 0042, Pg. 7 ¶ 0059, Pg. 7 ¶ 0061 - Pg. 8 ¶ 0064) configured to determine, for a clustering parameter value of a clustering parameter, (Davidson et al., Fig. 3, Pg. 3 ¶ 0029, Pg. 4 ¶ 0041 - Pg. 5 ¶ 0042, Pg. 5 ¶ 0044 - 0045, Pg. 6 ¶ 0053 and 0055 [“clustering algorithm clusters the datapoints into X times Y clusters, wherein X is the number of groups (e.g., the number of user classification labels) into which a user wants to classify the images; and Y is greater than or equal to 1 (e.g., 3, 4, 5)”]) a respective set of clusters within the plurality of images based on the generated image embeddings; (Davidson et al., Abstract, Figs. 1, 3, 4 & 6, Pg. 1 ¶ 0005, Pg. 3 ¶ 0029 - 0031, Pg. 4 ¶ 0033, Pg. 5 ¶ 0042 - 0046, Pg. 7 ¶ 0059) an evaluation module (Davidson et al., Abstract, Figs. 1, 3, 4, 6 & 8, Pg. 1 ¶ 0005 and 0016, Pg. 2 ¶ 0021 - 0023, Pg. 3 ¶ 0029 - 0031, Pg. 4 ¶ 0037 and 0041, Pg. 6 ¶ 0053, Pg. 7 ¶ 0056 and 0059, Pg. 7 ¶ 0061 - Pg. 8 ¶ 0064) configured to construct a parameter space and determine a measure of the parameter space; (Davidson et al., Figs. 3 - 6, Pg. 1 ¶ 0005 - 0006, Pg. 3 ¶ 0030, Pg. 4 ¶ 0033 - 0034 and 0040, Pg. 5 ¶ 0045, Pg. 6 ¶ 0048 and 0051 - 0053 [“third region 530 to display a list of the identified clusters”]) and a user interface (Davidson et al., Abstract, Figs. 1 & 5A - 8, Pg. 1 ¶ 0006 and 0016, Pg. 2 ¶ 0021 - 0023, Pg. 3 ¶ 0029 - 0030, Pg. 4 ¶ 0033 - 0034, Pg. 5 ¶ 0043 - 0044, Pg. 5 ¶ 0047 - Pg. 6 ¶ 0048, Pg. 7 ¶ 0058 - Pg. 8 ¶ 0064) configured to receive a user input. (Davidson et al., Abstract, Figs. 5A - 7, Pg. 1 ¶ 0005 - 0006, Pg. 2 ¶ 0023, Pg. 3 ¶ 0030 - 0031, Pg. 4 ¶ 0033 - 0034, Pg. 5 ¶ 0044 and 0047, Pg. 6 ¶ 0055 - Pg. 7 ¶ 0056, Pg. 7 ¶ 0058 - 0060) Davidson et al. fail to disclose explicitly determining, separately for each of a plurality of clustering parameter values, a respective set of clusters; constructing a trajectory in a parameter space, wherein one dimension of the parameter space represents the plurality of clustering parameter values and another dimension of the parameter space is based on the number of clusters within the set of clusters determined by the clustering module when using a respective clustering parameter value of the plurality of clustering parameter values; determining a measure of the parameter space between the origin of the parameter space and the trajectory; and indicating changes and/or effects of the user input on/in the measure. Pertaining to analogous art, Quadri et al. disclose a computing device (Quadri et al., Pg. 1 § 1 ¶ 6 - 7, Pg. 2 Fig. 1, Pg. 3 § 3 ¶ 1, Pg. 3 Fig. 2, Pg. 6 § 4 ¶ 1 - 2, Pg. 7 § 5.1 - § 5.2, Pg. 7 Figs. 6 & 7, Pg. 11 Subsection “Analysis Software”, Pg. 13 § 9) and a computer-implemented method for preparing training data, (Quadri et al., Pg. 1 § 1 ¶ 6 - 7, Pg. 2 Fig. 1, Pg. 3 § 3 ¶ 1, Pg. 3 Fig. 2, Pg. 6 § 4 ¶ 1 - 2, Pg. 7 § 5.1 - § 5.2, Pg. 7 Figs. 6 & 7, Pg. 11 Subsection “Analysis Software”, Pg. 13 § 9) comprising: receiving a plurality of images; (Quadri et al., Pg. 3 § 3, Pg. 3 Fig. 2, Pg. 6 § 4 ¶ 3, Pg. 8 § 6.2 [“We selected eight representative datasets with clustering structures (MNIST ( n = 70000) [88]”. The Examiner asserts that the MNIST dataset is a dataset comprised of a plurality of images.]) determining, separately for each of a plurality of clustering parameter values of a clustering parameter, a respective set of clusters within the plurality of images based on the generated image embeddings; (Quadri et al., Pg. 2 Fig. 1, Pg. 3 § 3, Pg. 3 Fig. 2, Pg. 5 § 3.2 - Pg. 7 Subsection “Optimized Design”, Pg. 5 Fig. 3, Pg. 6 Figs. 4 & 5) constructing a trajectory in a parameter space, (Quadri et al., Pg. 1 § 1 ¶ 4 - 6, Pg. 3 § 3, Pg. 3 Fig. 2, Pg. 5 § 3.3 - Pg. 6 § 3.3.2, Pg. 5 Fig. 3, Pg. 6 Figs. 4 & 5, Pg. 7 Subsection “Scatterplot Rendering” - Subsection “Saliency Computation” [“we take the generated scatterplots and compute threshold plots and a saliency score. The threshold plot is a monotonic step function, where the horizontal axis encodes values that describe the separation of clusters, while the vertical axis describes the number of clusters visible at that threshold. We extract from this plot the number of clusters an individual is likely to see and exactly how salient those clusters are”]) wherein one dimension of the parameter space represents the plurality of clustering parameter values and another dimension of the parameter space is based on the number of clusters within the set of clusters determined by the clustering module when using a respective clustering parameter value of the plurality of clustering parameter values; (Quadri et al., Pg. 1 § 1 ¶ 4 - 6, Pg. 3 § 3, Pg. 3 Fig. 2, Pg. 5 § 3.3 - Pg. 6 § 3.3.2, Pg. 5 Fig. 3, Pg. 6 Figs. 4 & 5, Pg. 7 Subsection “Scatterplot Rendering” - Subsection “Saliency Computation” [“we take the generated scatterplots and compute threshold plots and a saliency score. The threshold plot is a monotonic step function, where the horizontal axis encodes values that describe the separation of clusters, while the vertical axis describes the number of clusters visible at that threshold. We extract from this plot the number of clusters an individual is likely to see and exactly how salient those clusters are” and “(e) A threshold plot is generated from the merge tree. The horizontal axis represents the a persistence threshold of clusters, while the vertical axis shows the number of clusters visible at that threshold. The dashed red line shows how a threshold can be extracted from a given number of clusters and vice versa. Finally, the saliency for some number of clusters is measured as the range of the minimum and maximum threshold for that number of clusters”]) determining a measure of the parameter space between the origin of the parameter space and the trajectory; (Quadri et al., Pg. 3 § 3, Pg. 3 Fig. 2, Pg. 5 § 3.3 - Pg. 6 § 4 ¶ 2, Pg. 5 Fig. 3, Pg. 6 Figs. 4 & 5, Pg. 7 Subsection “Scatterplot Rendering” - Subsection “Optimized Design”, Pg. 8 § 6.3.1, Pg. 9 Fig. 10) and a user interface (Quadri et al., Pg. 1 § 1 ¶ 6 - 7, Pg. 2 Fig. 1, Pg. 3 § 3 ¶ 1, Pg. 3 Fig. 2, Pg. 6 § 4 ¶ 1 - 2, Pg. 9 § 6.3.4, Pg. 12 § 7, Pg. 13 § 9) configured to receive a user input and to indicate changes and/or effects of the user input on/in the measure. (Quadri et al., Pg. 1 § ¶ 5 - 7, Pg. 2 Fig. 1, Pg. 3 § 3, Pg. 3 Fig. 2, Pg. 6 § 3.3.2 - § 4 ¶ 2, Pg. 7 Subsection “Scatterplot Rendering” - Subsection “Optimized Design”) Davidson et al. and Quadri et al. are combinable because they are both directed towards systems and methods that identify clusters within a dataset of images. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Davidson et al. with the teachings of Quadri et al. This modification would have been prompted in order to enhance the base device of Davidson et al. with the well-known and applicable technique Quadri et al. applied to a similar device. Determining a respective set of clusters separately for each of a plurality of clustering parameter values, constructing a trajectory in a parameter space, wherein one dimension of the parameter space represents the plurality of clustering parameter values and another dimension of the parameter space is based on the number of clusters within the set of clusters determined when using a respective clustering parameter value of the plurality of clustering parameter values, determining a measure of the parameter space between the origin of the parameter space and the trajectory and indicating changes and/or effects of the user input on/in the measure, as taught by Quadri et al., would enhance the base device of Davidson et al. by enabling it to automatically identify and provide to a user the most salient set(s) of clusters from a plurality of respective sets of clusters separately determined based on the plurality of images so as to improve its ability to aid users in accurately and efficiently preparing training datasets useable for training a machine learning algorithm to reliably classify images. Furthermore, this modification would have been prompted by the teachings and suggestions of Quadri et al. that their technique reduces ambiguity in the data, reduces the chance of misinterpretation, improves cluster task performance and reveals the most salient cluster structure automatically, see at least page 1 the abstract, page 1 section 1 paragraphs 4 - 5, page 12 section 8 paragraph 1 and page 13 section 9 of Quadri et al. This combination could be completed according to well-known techniques in the art and would likely yield predictable results, in that the base device of Davidson et al. would utilize the technique disclosed by Quadri et al. to automatically identify and suggest to a user the most salient set(s) of clusters from a plurality of respective sets of clusters separately determined based on the plurality of images so as to improve its ability to assist users in quickly and accurately preparing training datasets that are useable for training a machine learning algorithm to reliably classify images. Therefore, it would have been obvious to combine Davidson et al. with Quadri et al. to obtain the invention as specified in claims 1 and 11.
- With regards to claim 2, Davidson et al. in view of Quadri et al. disclose the computing device as set forth in claim 1, wherein the clustering parameter is a clustering threshold. (Davidson et al., Fig. 3, Pg. 3 ¶ 0029, Pg. 4 ¶ 0041 - Pg. 5 ¶ 0042, Pg. 5 ¶ 0044 - 0045, Pg. 6 ¶ 0053 and 0055 [“clustering algorithm clusters the datapoints into X times Y clusters, wherein X is the number of groups (e.g., the number of user classification labels) into which a user wants to classify the images; and Y is greater than or equal to 1 (e.g., 3, 4, 5)”]) In addition, analogous art Quadri et al. disclose wherein the clustering parameter is a clustering threshold. (Quadri et al., Pg. 2 Fig. 1, Pg. 5 § 3.3 - Pg. 6 § 4 ¶ 2, Pg. 5 Fig. 3, Pg. 6 Figs. 4 & 5)
- With regards to claim 3, Davidson et al. in view of Quadri et al. disclose the computing device as set forth in claim 1. Davidson et al. fail to disclose explicitly wherein the parameter space is two-dimensional, the trajectory is a one-dimensional curve therein, and wherein the measure is an area under the curve. Pertaining to analogous art, Quadri et al. disclose wherein the parameter space is two-dimensional, (Quadri et al., Pg. 3 Fig. 2, Pg. 5 § 3.3 - Pg. 6 § 3.3.2, Pg. 5 Fig. 3, Pg. 6 Figs. 4 & 5, Pg. 8 § 6.3.1, Pg. 8 Fig. 9) the trajectory is a one-dimensional curve therein, (Quadri et al., Pg. 3 Fig. 2, Pg. 5 § 3.3 - Pg. 6 § 3.3.2, Pg. 5 Fig. 3, Pg. 6 Figs. 4 & 5, Pg. 8 § 6.3.1, Pg. 8 Fig. 9) and wherein the measure is an area under the curve. (Quadri et al., Pg. 8 § 6.3.1, Pg. 8 Fig. 9, Pg. 9 Fig. 10, Pg. 10 § 6.4.1 ¶ 1 - 4)
- With regards to claim 4, Davidson et al. in view of Quadri et al. disclose the computing device as set forth in claim 1, wherein the user interface is configured to receive user input indicating the addition or removal of at least one image to or from the plurality of images received by the input interface. (Davidson et al., Figs. 3, 5C, 5F & 6, Pg. 3 ¶ 0031, Pg. 5 ¶ 0044 and 0047, Pg. 6 ¶ 0055, Pg. 7 ¶ 0058)
- With regards to claim 6, Davidson et al. in view of Quadri et al. disclose the computing device as set forth in claim 1, wherein the user interface, UI (150), is further configured to prompt a user (10) to input the desired value of the measure (AUC) of the parameter space and/or to specify the desired tolerance interval. (Davidson et al., Fig. 3, Pg. 3 ¶ 0029, Pg. 4 ¶ 0041 - Pg. 5 ¶ 0044) In addition, analogous art Quadri et al. disclose wherein the user interface, UI (150), is further configured to prompt a user (10) to input the desired value of the measure (AUC) of the parameter space and/or to specify the desired tolerance interval. (Quadri et al., Pg. 1 § ¶ 5 - 7, Pg. 2 Fig. 1, Pg. 3 § 3, Pg. 3 Fig. 2, Pg. 6 § 3.3.2 - § 4 ¶ 2, Pg. 7 Subsection “Scatterplot Rendering” - Subsection “Optimized Design”)
- With regards to claims 10 and 13, Davidson et al. in view of Quadri et al. disclose the computing device and the method as set forth in claims 1 and 11, respectively, further comprising a visualization module (Davidson et al., Figs. 1, 3, 6 & 8, Pg. 1 ¶ 0005 and 0016, Pg. 2 ¶ 0021 - 0023, Pg. 3 ¶ 0027 - 0028, Pg. 4 ¶ 0037 and 0039 - 0040, Pg. 7 ¶ 0059 - Pg. 8 ¶ 0064) configured to perform a dimensional reduction on the image embeddings generated by the image embeddings generating module into a two-dimensional reduced parameter space; (Davidson et al., Figs. 1, 3, 4 & 6, Pg. 1 ¶ 0005 - 0006, Pg. 3 ¶ 0027 - 0028, Pg. 4 ¶ 0033 and 0039 - 0040, Pg. 5 ¶ 0045, Pg. 7 ¶ 0059 - 0060) wherein the user interface comprises a display (Davidson et al., Figs. 4 - 8, Pg. 1 ¶ 0005 - 0006 and 0016, Pg. 2 ¶ 0021 - 0023, Pg. 5 ¶ 0047) configured to indicate positions of images within the two-dimensional reduced parameter space. (Davidson et al., Figs. 4 - 5F, Pg. 1 ¶ 0005 - 0006, Pg. 3 ¶ 0030, Pg. 4 ¶ 0033 - 0034 and 0040, Pg. 5 ¶ 0043 and 0045 - 0046, Pg. 6 ¶ 0048 - 0050, Pg. 7 ¶ 0059) In addition, analogous art Quadri et al. disclose performing a dimensional reduction on the image embeddings generated by the image embeddings generating module into a two-dimensional reduced parameter space; (Quadri et al., Pg. 5 § 3.2, Pg. 6 § 4 ¶ 1 - 3 [“For datasets with dimensionality higher than two, we first transformed them into 2D data using t-SNE and normalized them to [0,1] x [0,1]”]) wherein the user interface comprises a display configured to indicate positions of images within the two-dimensional reduced parameter space. (Quadri et al., Pg. 2 Fig. 1, Pg. 3 § 3 ¶ 1, Pg. 3 Fig. 2, Pg. 5 Fig. 3, Pg. 6 § 3.4 - Pg. 7 Subsection “Optimized Design”, Pg. 6 Fig. 5, Pg. 12 § 8 ¶ 1, Pg. 13 Fig. 15)
- With regards to claim 15, Davidson et al. in view of Quadri et al. disclose the method as set forth in claim 11 ([The Examiner asserts that Davidson et al. in view of Quadri et al. disclose the method as set forth in claim 11, see analysis of claim 11 included herein above.]) and a computer program product comprising executable program code configured to, when executed, perform (Davidson et al., Abstract, Figs. 1 & 8, Pg. 1 ¶ 0016 - Pg. 2 ¶ 0017, Pg. 2 ¶ 0021 - 0023, Pg. 7 ¶ 0061 - Pg. 8 ¶ 0063) the method as set forth in claim 11. ([The Examiner asserts that Davidson et al. in view of Quadri et al. disclose the method as set forth in claim 11, see analysis of claim 11 included herein above.])
Allowable Subject Matter
Claims 5, 7 - 9, 12 and 14 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: the prior art does not anticipate nor does it suggest the combination as presently claimed. In particular, determining a respective set of clusters based on generated image embeddings of a plurality of images for each of a plurality of clustering parameter values of a clustering parameter, constructing a trajectory in a parameter space, wherein one dimension of the parameter space represents the plurality of clustering parameter values and another dimension of the parameter space is based on the number of clusters determined using a respective clustering parameter value of the plurality of clustering parameter values, determining a measure of the parameter space between the origin of the parameter space and the trajectory, obtaining a desired value of the measure of the parameter space and generating an adapted set of images by removing images from the plurality of images and/or adding images to the plurality of images such that the measure determined based on the adapted set of images lies within a desired tolerance interval around the desired value of the measure. These elements, in combination with the remaining component(s) of the claim(s), are not taught nor are they suggested by the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Choudhary et al. U.S. Publication No. 2022/0292809 A1; which is directed towards methods and systems for clustering images, wherein feature vectors are generated for each of a plurality of images and the plurality of images are clustered based on the generated feature vectors and a variable threshold distance set by a user.
Levner et al. U.S. Patent No. 11,003,959; which is directed towards a method and system for clustering image data, wherein a feature embedding is generated for each of a plurality of images, the plurality of images are embedded into an image embedding space and clustered based on the generated feature embeddings, vector norm values are determined for each of the plurality of images and one or more images may be removed from the image embedding space based on the determined vector norm values.
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/ERIC RUSH/Primary Examiner, Art Unit 2677
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