DETAILED ACTION
1. 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
2. The information disclosure statement (IDS) submitted on 07/30/2024 has been received, entered into the record, and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 112
3. 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.
4. 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.
5. Claims 3, 10, and 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Specifically, it is unclear as to how the claimed output is a novel solution to thew problem, when novelty is a legal determination that is reached from patent professionals and/or judicial authorities.
Claim Rejections - 35 USC § 101
6. 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.
7. Claims (1-7), (8-14), and (15-20) are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the 2019 PEG, when considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A prong 1), and if so, it must additionally be determined whether the claim is integrated into a practical application (step 2A prong 2). If an abstract idea is present in the claim without integration into a practical application, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself (step 2B).
In the instant case, claims (1-7), (8-14), and (15-20) are directed to a method, apparatus, and non-transitory computer-readable medium respectively. Thus, each of the claims falls within one of the four statutory categories. However, the claims also fall within the judicial exception of an abstract idea.
Under Step 2A Prong 1, the test is to identify whether the claims are “directed to” a judicial exception. The examiner notes that the claimed invention is directed to an abstract idea in that the instant application is directed to mental processes, specifically comprising between ideas.
The examiner further notes that claims (1-7), (8-14), and (15-20) recite a method, apparatus, and non-transitory computer-readable medium for comprising between ideas which is similar to themes defined above of method of mental processes such as performing the recommendation of information, and is similar to the abstract idea identified in the 2019 PEG in grouping “c” in that the claims recite certain methods of mental processes such as performing the comprising between ideas. The limitations, substantially comprising the body of the claim, recite a process of comprising between ideas. The examiner notes that the claimed invention comprises between ideas. Because the limitations above closely follow the steps in comprising between ideas, and the steps of the claims involve mental processes, the claim recites an abstract idea consistent with the “mental processes” grouping set forth in the 2019 PEG.
Claim 1:
A method for compromising between competing ideas, comprising: generating a group of embedded inputs by embedding each input of a group of inputs in an embedding space;
weighting each embedded input of the group of embedded inputs based on one or more parameters;
calculating a weighted centroid based on weighting each embedded input; and
generating, via a generative model, an output based on the weighted centroid.
These limitations, as drafted, is an apparatus that, under its broadest reasonable interpretation, covers the performance of mental processes specifically comprising between ideas. Comprising between ideas has long before the modern computer was invented, and continues to be predominantly a product of human endeavor. The instant application is directed to comprising between ideas. Additionally, the generation of embeddings for a group of inputs via the use of an embedded space can be performed by a human via their mind and/or pen & paper. Furthermore, the weighting of each input via the use of one or more parameters can be performed by a human via their mind and/or pen & paper. Moreover, the calculation of weighted centroid based on each embedded input can be performed by a human via their mind and/or pen & paper. Additionally, the generation of an output based on the weighted centroid can be performed by a human via their mind and/or pen & paper. Because the limitations above closely follow the steps of comprising between ideas, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind and/or pen & paper, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the 2019 PEG.
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. The Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application.
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to an apparatus instructing the reader to implement the identified apparatus of mental processes of comprising between ideas. The elements of the claim do not themselves amount to an improvement to the computer, to a technology or another technical field.
Here, the claim elements entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the claims have failed to integrate a practical application (see at least 84 Fed. Reg. (4) at 55). Under the 2019 PEG, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that claim 1 does not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims do not recite anything that is found to render the abstract idea as being transformed into a patent eligible invention. The dependent claims are merely reciting further embellishments of the abstract idea and do not claim anything that amounts to significantly more than the abstract idea itself.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea. Claims 2-7 are directed to further embellishments of the central theme of the abstract idea in that the claims are directed to further embellishments of the comprising between ideas of the steps of claim 1 and do not amount to significantly more.
Specifically, claim 2 recites the defining of the inputs which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 3 recites the defining of the generated output which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 4 recites the defining of the parameters which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 5 recites the defining of a group of factors which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 6 recites the defining of an initial centroid which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 7 recites the defining of the generated output which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Claim 8:
An apparatus for compromising between competing ideas, 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: generate a group of embedded inputs by embedding each input of a group of inputs in an embedding space;
weight each embedded input of the group of embedded inputs based on one or more parameters;
calculate a weighted centroid based on weighting each embedded input; and
generate, via a generative model, an output based on the weighted centroid.
These limitations, as drafted, is an apparatus that, under its broadest reasonable interpretation, covers the performance of mental processes specifically comprising between ideas. Comprising between ideas has long before the modern computer was invented, and continues to be predominantly a product of human endeavor. The instant application is directed to comprising between ideas. Additionally, the generation of embeddings for a group of inputs via the use of an embedded space can be performed by a human via their mind and/or pen & paper. Furthermore, the weighting of each input via the use of one or more parameters can be performed by a human via their mind and/or pen & paper. Moreover, the calculation of weighted centroid based on each embedded input can be performed by a human via their mind and/or pen & paper. Additionally, the generation of an output based on the weighted centroid can be performed by a human via their mind and/or pen & paper. Because the limitations above closely follow the steps of comprising between ideas, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind and/or pen & paper, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the 2019 PEG.
The mere nominal recitation of generic computing components such as a “one or more processors” and “one or more memories” do not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. The Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application.
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to an apparatus instructing the reader to implement the identified apparatus of mental processes of comprising between ideas. The elements of the claim do not themselves amount to an improvement to the computer, to a technology or another technical field.
Here, the claim elements entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the claims have failed to integrate a practical application (see at least 84 Fed. Reg. (4) at 55). Under the 2019 PEG, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that claim 8 does not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims do not recite anything that is found to render the abstract idea as being transformed into a patent eligible invention. The dependent claims are merely reciting further embellishments of the abstract idea and do not claim anything that amounts to significantly more than the abstract idea itself.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea. Claims 9-14 are directed to further embellishments of the central theme of the abstract idea in that the claims are directed to further embellishments of the comprising between ideas of the steps of claim 8 and do not amount to significantly more.
Specifically, claim 9 recites the defining of the inputs which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 10 recites the defining of the generated output which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 11 recites the defining of the parameters which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 12 recites the defining of a group of factors which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 13 recites the defining of an initial centroid which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 14 recites the defining of the generated output which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Claim 15:
A non-transitory computer-readable medium having program code recorded thereon for compromising between competing ideas, the program code executed by a processor and comprising: program code to generate a group of embedded inputs by embedding each input of a group of inputs in an embedding space;
program code to weight each embedded input of the group of embedded inputs based on one or more parameters;
program code to calculate a weighted centroid based on weighting each embedded input; and
program code to generate, via a generative model, an output based on the weighted centroid.
These limitations, as drafted, is an apparatus that, under its broadest reasonable interpretation, covers the performance of mental processes specifically comprising between ideas. Comprising between ideas has long before the modern computer was invented, and continues to be predominantly a product of human endeavor. The instant application is directed to comprising between ideas. Additionally, the generation of embeddings for a group of inputs via the use of an embedded space can be performed by a human via their mind and/or pen & paper. Furthermore, the weighting of each input via the use of one or more parameters can be performed by a human via their mind and/or pen & paper. Moreover, the calculation of weighted centroid based on each embedded input can be performed by a human via their mind and/or pen & paper. Additionally, the generation of an output based on the weighted centroid can be performed by a human via their mind and/or pen & paper. Because the limitations above closely follow the steps of comprising between ideas, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind and/or pen & paper, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the 2019 PEG.
The mere nominal recitation of generic computing components such as a “processor” does not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. The Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application.
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to an apparatus instructing the reader to implement the identified apparatus of mental processes of comprising between ideas. The elements of the claim do not themselves amount to an improvement to the computer, to a technology or another technical field.
Here, the claim elements entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the claims have failed to integrate a practical application (see at least 84 Fed. Reg. (4) at 55). Under the 2019 PEG, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that claim 15 does not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims do not recite anything that is found to render the abstract idea as being transformed into a patent eligible invention. The dependent claims are merely reciting further embellishments of the abstract idea and do not claim anything that amounts to significantly more than the abstract idea itself.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea. Claims 16-20 are directed to further embellishments of the central theme of the abstract idea in that the claims are directed to further embellishments of the comprising between ideas of the steps of claim 15 and do not amount to significantly more.
Specifically, claim 16 recites the defining of the inputs and the generated output which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 17 recites the defining of the parameters which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Additionally, claim 18 recites the defining of a group of factors which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 19 recites the defining of an initial centroid which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Moreover, claim 20 recites the defining of the generated output which can be performed by a human via their mind and/or pen & paper and does not amount to significantly more.
Claim Rejections - 35 USC § 102
8. 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.
9. 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.
10. Claims 1, 4, 6-8, 11, 13-15, 17, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Asano et al. (U.S. PGPUB 2023/0297624).
11. Regarding claims 1, 8, and 15, Asano teaches a method, apparatus, and non-transitory computer-readable medium comprising:
A) generating a group of embedded inputs by embedding each input of a group of inputs in an embedding space (Paragraphs 150 and 151);
B) weighting each embedded input of the group of embedded inputs based on one or more parameters (Paragraph 153);
C) calculating a weighted centroid based on weighting each embedded input (Paragraph 153); and
D) generating, via a generative model, an output based on the weighted centroid (Paragraphs 153 and 170, Figures 3a-3b).
The examiner notes that Asano teaches “generating a group of embedded inputs by embedding each input of a group of inputs in an embedding space” as “all content items in the corpus of content items (e.g., retrieved by search module 112) are vectorized by vector module 114. As used herein, vectorization (generating vector representations) refers to mapping a content item to a numerical representation comprising an array of values. Vectorization has the benefit of reducing the data size of a content item. Vectorization provides a quantitative format by which different content items can be compared to one another. In some embodiments, content items in content database 106 are analyzed and corresponding vector representations are generated as the content items are added to content database 106” (Paragraph 150) and “each generated vector representation for each content item in the corpus of content items comprises a set of dimensions representing the corresponding content item in the corpus. The set of dimensions may include numerical values that may serve as coordinates in a multi-dimensional vector space (i.e., the dimensionality being N, wherein N is the number of dimensions)” (Paragraph 151). The examiner further notes that the vectorization of inserted content items (i.e. the claimed inputs in the broadest reasonable interpretation) results in generated vectors (i.e. embeddings) in a vector space (i.e. embedding space). The examiner further notes that Asano teaches “weighting each embedded input of the group of embedded inputs based on one or more parameters” as “each vector can be weighted or otherwise configured to induce a different impact on centroid calculation and eventual visualization. Accordingly, the vector and/or underlying document can be weighted, wherein a weight (e.g., a constant or dynamic value) corresponds to the frequency of the corresponding search term, the prominence of the clinical trial sponsor, the timestamp (i.e., newer clinical trials may be afforded a heftier weighting), the clinical trial phase, the primary indication (i.e., whether the search keyword is a primary versus secondary indication), and other suitable metrics” (Paragraph 153). The examiner further notes that the weighting of the generated vectors is based off of various parameters (See example metrics). The examiner further notes that Asano teaches “calculating a weighted centroid based on weighting each embedded input” as “a centroid vector is calculated based on vector representations of content items. In some embodiments, in order to calculate the centroid vector, a relevant initial subset of content items is identified based on the search specification. In some embodiments, this relevant initial subset of content items is the set of content items from the corpus of content items that are keyword-based search matches (region 204 of FIG. 2). For example, with respect to the sinus pause example, this may include scientific articles in the established search space that explicitly mention sinus pause. The centroid vector for this example would be calculated based on the corresponding vector representations of these scientific articles that explicitly mention sinus pause. In some embodiments, the centroid vector is an average of the vector representations of the content items in the relevant initial subset of content items. With respect to the sinus pause example, this may include the average of the vector representations of the scientific articles that explicitly mention sinus pause. The average can be calculated on an element-by-element basis. Vectorization display element 304 shows an example of a centroid vector for the sinus pause example. Each element of the centroid vector in vectorization display element 304 may be an average of corresponding elements of vector representations of scientific articles, in the established search space, that explicitly mention sinus pause. This average may be a simple numerical average of each element of the vector representations to determine a new average vector. It is also possible to employ different types of averages or different types of vector aggregation methods to calculate the centroid vector (e.g., by summing the vector representations of the content items, using machine learning to train a model to calculate the best centroid vector to represent a subset of content items, etc.). As a non-limiting example, a weighted average is utilized. With respect to the sinus pause example, each document can be weighted according to a specified document property, such as the frequency of the search term “sinus pause” in each document. Various other weighting approaches can also be adopted. For example, each vector can be weighted or otherwise configured to induce a different impact on centroid calculation and eventual visualization. Accordingly, the vector and/or underlying document can be weighted, wherein a weight (e.g., a constant or dynamic value) corresponds to the frequency of the corresponding search term, the prominence of the clinical trial sponsor, the timestamp (i.e., newer clinical trials may be afforded a heftier weighting), the clinical trial phase, the primary indication (i.e., whether the search keyword is a primary versus secondary indication), and other suitable metrics. Therefore, the centroid may be “pulled closer” to the more weighted vector(s) based on the aforementioned weighting factors. In various embodiments, a user is also able to interact with vectorization display element 304 to display vectors other than the centroid vector (e.g., by clicking on different items of visualization element 306)” (Paragraph 153). The examiner further notes that a weighted average calculated centroid that is based off of weighted vectors teaches the claimed weighted centroid. The examiner further notes that Asano teaches “generating, via a generative model, an output based on the weighted centroid” as “a centroid vector is calculated based on vector representations of content items. In some embodiments, in order to calculate the centroid vector, a relevant initial subset of content items is identified based on the search specification. In some embodiments, this relevant initial subset of content items is the set of content items from the corpus of content items that are keyword-based search matches (region 204 of FIG. 2). For example, with respect to the sinus pause example, this may include scientific articles in the established search space that explicitly mention sinus pause. The centroid vector for this example would be calculated based on the corresponding vector representations of these scientific articles that explicitly mention sinus pause. In some embodiments, the centroid vector is an average of the vector representations of the content items in the relevant initial subset of content items. With respect to the sinus pause example, this may include the average of the vector representations of the scientific articles that explicitly mention sinus pause. The average can be calculated on an element-by-element basis. Vectorization display element 304 shows an example of a centroid vector for the sinus pause example. Each element of the centroid vector in vectorization display element 304 may be an average of corresponding elements of vector representations of scientific articles, in the established search space, that explicitly mention sinus pause. This average may be a simple numerical average of each element of the vector representations to determine a new average vector. It is also possible to employ different types of averages or different types of vector aggregation methods to calculate the centroid vector (e.g., by summing the vector representations of the content items, using machine learning to train a model to calculate the best centroid vector to represent a subset of content items, etc.). As a non-limiting example, a weighted average is utilized. With respect to the sinus pause example, each document can be weighted according to a specified document property, such as the frequency of the search term “sinus pause” in each document. Various other weighting approaches can also be adopted. For example, each vector can be weighted or otherwise configured to induce a different impact on centroid calculation and eventual visualization. Accordingly, the vector and/or underlying document can be weighted, wherein a weight (e.g., a constant or dynamic value) corresponds to the frequency of the corresponding search term, the prominence of the clinical trial sponsor, the timestamp (i.e., newer clinical trials may be afforded a heftier weighting), the clinical trial phase, the primary indication (i.e., whether the search keyword is a primary versus secondary indication), and other suitable metrics. Therefore, the centroid may be “pulled closer” to the more weighted vector(s) based on the aforementioned weighting factors. In various embodiments, a user is also able to interact with vectorization display element 304 to display vectors other than the centroid vector (e.g., by clicking on different items of visualization element 306)” (Paragraph 153) and “At 412, the determined relative relevancies may be used to provide an interactive graphical visualization of at least a portion or segment of the relevant initial subset of content items in the group of content items and one or more other content items in the group of content items. Thus, in effect, the interactive graphical visualization may include: (1) a portion or segment of the relevant initial subset of content items in the group of content items, e.g., content items unearthed via a keyword-based search; (2) one or more other content items in the group of content items, e.g., content items unearthed via vector-based analysis; or (3) a combination of (1) and (2). The “portion” or “segment” of the relevant initial subset of content items may refer to a subset of content items constrained by a parameter, e.g., the preferred number of keyword-based search results to be populated on the interactive graphical visualization, a secondary filter further constricting results in addition to the search specification, or other suitable parameters. In some embodiments, visualization module 118 provides the interactive graphical visualization to client 102 via network 104” (Paragraph 170). The examiner further notes that an interactive visualization is “generated” by a visualization module (i.e. the claimed undefined model in the broadest reasonable interpretation). Such an interactive visualization is based off of the weighted centroid.
Regarding claims 4, 11, and 17, Asano teaches a method, apparatus, and non-transitory computer-readable medium comprising:
A) wherein the one or more parameters include a distance from an initial centroid and/or one or more factor values (Paragraph 153).
The examiner notes that Asano teaches “wherein the one or more parameters include a distance from an initial centroid and/or one or more factor values” as “each vector can be weighted or otherwise configured to induce a different impact on centroid calculation and eventual visualization. Accordingly, the vector and/or underlying document can be weighted, wherein a weight (e.g., a constant or dynamic value) corresponds to the frequency of the corresponding search term, the prominence of the clinical trial sponsor, the timestamp (i.e., newer clinical trials may be afforded a heftier weighting), the clinical trial phase, the primary indication (i.e., whether the search keyword is a primary versus secondary indication), and other suitable metrics” (Paragraph 153). The examiner further notes that the weighting of the generated vectors is based off of various parameters that include one or more factor values (See example of frequency and/or timestamp).
Regarding claims 6, 13, and 19, Asano teaches a method, apparatus, and non-transitory computer-readable medium comprising:
A) wherein the initial centroid is based on the group of embedded inputs, prior to weighting each embedded input (Paragraph 153).
The examiner notes that Asano teaches “wherein the initial centroid is based on the group of embedded inputs, prior to weighting each embedded input” as “each vector can be weighted or otherwise configured to induce a different impact on centroid calculation and eventual visualization. Accordingly, the vector and/or underlying document can be weighted, wherein a weight (e.g., a constant or dynamic value) corresponds to the frequency of the corresponding search term, the prominence of the clinical trial sponsor, the timestamp (i.e., newer clinical trials may be afforded a heftier weighting), the clinical trial phase, the primary indication (i.e., whether the search keyword is a primary versus secondary indication), and other suitable metrics” (Paragraph 153). The examiner further notes that due to the diction of “and/or” in parent dependent claims 4, 11, and 17 respectively, the claimed initial centroid is purely optional and does not trigger when parent dependent claims 4, 11, and 17 are interpreted in the broadest reasonable interpretation as being directed towards the one or more parameters including one or more factor values.
Regarding claims 7, 14, and 20, Asano teaches a method, apparatus, and non-transitory computer-readable medium comprising:
A) wherein the output is different than each input of the group of inputs (Paragraphs 151, 153, and 170, Figures 3a-3b).
The examiner notes that Asano teaches “wherein the output is different than each input of the group of inputs” as “all content items in the corpus of content items (e.g., retrieved by search module 112) are vectorized by vector module 114. As used herein, vectorization (generating vector representations) refers to mapping a content item to a numerical representation comprising an array of values. Vectorization has the benefit of reducing the data size of a content item. Vectorization provides a quantitative format by which different content items can be compared to one another. In some embodiments, content items in content database 106 are analyzed and corresponding vector representations are generated as the content items are added to content database 106” (Paragraph 150) and “each generated vector representation for each content item in the corpus of content items comprises a set of dimensions representing the corresponding content item in the corpus. The set of dimensions may include numerical values that may serve as coordinates in a multi-dimensional vector space (i.e., the dimensionality being N, wherein N is the number of dimensions)” (Paragraph 151), “a centroid vector is calculated based on vector representations of content items. In some embodiments, in order to calculate the centroid vector, a relevant initial subset of content items is identified based on the search specification. In some embodiments, this relevant initial subset of content items is the set of content items from the corpus of content items that are keyword-based search matches (region 204 of FIG. 2). For example, with respect to the sinus pause example, this may include scientific articles in the established search space that explicitly mention sinus pause. The centroid vector for this example would be calculated based on the corresponding vector representations of these scientific articles that explicitly mention sinus pause. In some embodiments, the centroid vector is an average of the vector representations of the content items in the relevant initial subset of content items. With respect to the sinus pause example, this may include the average of the vector representations of the scientific articles that explicitly mention sinus pause. The average can be calculated on an element-by-element basis. Vectorization display element 304 shows an example of a centroid vector for the sinus pause example. Each element of the centroid vector in vectorization display element 304 may be an average of corresponding elements of vector representations of scientific articles, in the established search space, that explicitly mention sinus pause. This average may be a simple numerical average of each element of the vector representations to determine a new average vector. It is also possible to employ different types of averages or different types of vector aggregation methods to calculate the centroid vector (e.g., by summing the vector representations of the content items, using machine learning to train a model to calculate the best centroid vector to represent a subset of content items, etc.). As a non-limiting example, a weighted average is utilized. With respect to the sinus pause example, each document can be weighted according to a specified document property, such as the frequency of the search term “sinus pause” in each document. Various other weighting approaches can also be adopted. For example, each vector can be weighted or otherwise configured to induce a different impact on centroid calculation and eventual visualization. Accordingly, the vector and/or underlying document can be weighted, wherein a weight (e.g., a constant or dynamic value) corresponds to the frequency of the corresponding search term, the prominence of the clinical trial sponsor, the timestamp (i.e., newer clinical trials may be afforded a heftier weighting), the clinical trial phase, the primary indication (i.e., whether the search keyword is a primary versus secondary indication), and other suitable metrics. Therefore, the centroid may be “pulled closer” to the more weighted vector(s) based on the aforementioned weighting factors. In various embodiments, a user is also able to interact with vectorization display element 304 to display vectors other than the centroid vector (e.g., by clicking on different items of visualization element 306)” (Paragraph 153), and “At 412, the determined relative relevancies may be used to provide an interactive graphical visualization of at least a portion or segment of the relevant initial subset of content items in the group of content items and one or more other content items in the group of content items. Thus, in effect, the interactive graphical visualization may include: (1) a portion or segment of the relevant initial subset of content items in the group of content items, e.g., content items unearthed via a keyword-based search; (2) one or more other content items in the group of content items, e.g., content items unearthed via vector-based analysis; or (3) a combination of (1) and (2). The “portion” or “segment” of the relevant initial subset of content items may refer to a subset of content items constrained by a parameter, e.g., the preferred number of keyword-based search results to be populated on the interactive graphical visualization, a secondary filter further constricting results in addition to the search specification, or other suitable parameters. In some embodiments, visualization module 118 provides the interactive graphical visualization to client 102 via network 104” (Paragraph 170). The examiner further notes that an interactive visualization is “different” then each content item of the inserted content items (i.e. the claimed group of inputs).
Claim Rejections - 35 USC § 103
12. 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.
13. 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.
14. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
15. Claims 2-3, 9-10, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Asano et al. (U.S. PGPUB 2023/0297624) as applied to claims 1, 4, 6-8, 11, 13-15, 17, and 19-20 above, and further in view of Dubey et al. (Article entitled “AI Assisted Apparel Design”, dated 10 July 2020).
16. Regarding claims 2 and 9, Asano does not explicitly teach a method and an apparatus comprising:
A) wherein each input of the group of inputs is a solution to a problem.
Dubey, however, teaches “wherein each input of the group of inputs is a solution to a problem” as “Apparel-Style-Merge assistant works on two high level steps: 1. segmentation of input designs, and 2. reconstruction of new designs by placing segmented parts from multiple apparels at the appropriate places… While creating new designs, the designers may use AI assistants Apparel-Style-Merge Assistant (as shown in Figure 3 and Figure 4)” (Section 3.1) and “Figure 3: Apparel-Style-Merge Assistant: Designer can select multiple apparels and using Apparel-Style-Merge assistant generate new design. The AI assistant segments the input apparels and generates new design by combining various segments” (Figure 3).
The examiner further notes that although Asano clearly stores multiple content items that are then input into its system, there is no explicit teaching that such input items are solutions to a problem. Nevertheless, Dubey teaches the concept of inputs being various “designs” (i.e. the claimed undefined solution to a problem in the broadest reasonable interpretation). Specifically, clothing designs are “solutions” to a problem of needed fashion in the broadest reasonable interpretation. The combination would result in the content items that are input of Asano to be expanded to also include solutions to a problem.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Dubey’s would have allowed Asano’s to provide a method for creating new designs, as noted by Dubey (Section 3).
Regarding claims 3 and 10, Asano does not explicitly teach a method and an apparatus comprising:
A) wherein the output is a novel solution to the problem.
Dubey, however, teaches “wherein the output is a novel solution to the problem” as “Apparel-Style-Merge assistant works on two high level steps: 1. segmentation of input designs, and 2. reconstruction of new designs by placing segmented parts from multiple apparels at the appropriate places… While creating new designs, the designers may use AI assistants Apparel-Style-Merge Assistant (as shown in Figure 3 and Figure 4)” (Section 3.1) and “Figure 3: Apparel-Style-Merge Assistant: Designer can select multiple apparels and using Apparel-Style-Merge assistant generate new design. The AI assistant segments the input apparels and generates new design by combining various segments” (Figure 3).
The examiner further notes that although Asano clearly stores multiple content items that are then input into its system to produce an output, there is no explicit teaching that such an output is a novel solution. Nevertheless, Dubey teaches the concept of inputs being various “designs” (i.e. the claimed undefined solution to a problem in the broadest reasonable interpretation) that subsequently produces a new (i.e. novel) design. The combination would result in the content items that are input of Asano to be expanded to also include solutions to a problem to subsequently produce an output that is a novel design (i.e. solution).
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Dubey’s would have allowed Asano’s to provide a method for creating new designs, as noted by Dubey (Section 3).
Regarding claim 16, Asano does not explicitly teach a non-transitory computer-readable medium comprising:
A) wherein: each input of the group of inputs is a solution to a problem; and
B) the output is a novel solution to the problem.
Dubey, however, teaches “wherein: each input of the group of inputs is a solution to a problem” as “Apparel-Style-Merge assistant works on two high level steps: 1. segmentation of input designs, and 2. reconstruction of new designs by placing segmented parts from multiple apparels at the appropriate places… While creating new designs, the designers may use AI assistants Apparel-Style-Merge Assistant (as shown in Figure 3 and Figure 4)” (Section 3.1) and “Figure 3: Apparel-Style-Merge Assistant: Designer can select multiple apparels and using Apparel-Style-Merge assistant generate new design. The AI assistant segments the input apparels and generates new design by combining various segments” (Figure 3) and “the output is a novel solution to the problem” as “Apparel-Style-Merge assistant works on two high level steps: 1. segmentation of input designs, and 2. reconstruction of new designs by placing segmented parts from multiple apparels at the appropriate places… While creating new designs, the designers may use AI assistants Apparel-Style-Merge Assistant (as shown in Figure 3 and Figure 4)” (Section 3.1) and “Figure 3: Apparel-Style-Merge Assistant: Designer can select multiple apparels and using Apparel-Style-Merge assistant generate new design. The AI assistant segments the input apparels and generates new design by combining various segments” (Figure 3).
The examiner further notes that although Asano clearly stores multiple content items that are then input into its system to produce an output, there is no explicit teaching that such an output is a novel solution. Nevertheless, Dubey teaches the concept of inputs being various “designs” (i.e. the claimed undefined solution to a problem in the broadest reasonable interpretation) that subsequently produces a new (i.e. novel) design. The combination would result in the content items that are input of Asano to be expanded to also include solutions to a problem to subsequently produce an output that is a novel design (i.e. solution).
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Dubey’s would have allowed Asano’s to provide a method for creating new designs, as noted by Dubey (Section 3).
17. Claims 5, 12, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Asano et al. (U.S. PGPUB 2023/0297624) as applied to claims 1, 4, 6-8, 11, 13-15, 17, and 19-20 above, and further in view of El-Diraby et al. (U.S. 2017/0103402).
18. Regarding claims 5, 12, and 18, Asano further teaches a method, apparatus, and non-transitory computer-readable medium comprising:
A) wherein: each factor value of the one or more factor values is associated with a different factor of a group of factors (Paragraph 153).
The examiner notes that Asano teaches “wherein: each factor value of the one or more factor values is associated with a different factor of a group of factors” as “each vector can be weighted or otherwise configured to induce a different impact on centroid calculation and eventual visualization. Accordingly, the vector and/or underlying document can be weighted, wherein a weight (e.g., a constant or dynamic value) corresponds to the frequency of the corresponding search term, the prominence of the clinical trial sponsor, the timestamp (i.e., newer clinical trials may be afforded a heftier weighting), the clinical trial phase, the primary indication (i.e., whether the search keyword is a primary versus secondary indication), and other suitable metrics” (Paragraph 153). The examiner further notes that the weighting of the generated vectors is based off of various parameters that include one or more factor values that are associated with a different factor of a group of factors (See examples of frequency, timestamp, etc).
Asano does not explicitly teach:
B) the group of factors include at least one of cost, equity, diversity, or sustainability.
El-Diraby, however, teaches “the group of factors include at least one of cost, equity, diversity, or sustainability” as “analysis by the analysis engine 105 of a particular IDN relating to the Eglinton Crosstown project in Toronto, on Twitter will be described. Referring now to FIG. 8, shown therein is a graph showing possible stakeholder analysis data over time for a particular IDN, specifically relating to the Eglinton Crosstown project in Toronto. By selecting ‘sustainability’ as the context of analysis, collected tweets could be modeled and processed in the form of weighted vectors and aggregated in a monthly timeframe to generate the graph” (Paragraph 79).
The examiner further notes that although Asano clearly uses a multitude of factors as a basis for its weighting of vectors, there is no explicit teaching that such factors include at least one of cost, equity, diversity, and sustainability. Nevertheless, El-Diraby teaches the concept of the use of sustainability as a basis to weight vectors. The combination would result in expanding Asano to also use sustainability as one of its factors when weighting its vectors.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching El-Diraby’s would have allowed Asano’s to provide a method for semantically understanding a vector based on sustainability, as noted by El-Diraby (Paragraphs 77 and 79).
Conclusion
19. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. PGPUB 2024/0078290 issued to Osakabe et al. on 07 March 2024. The subject matter disclosed therein is pertinent to that of claims 1-20 (e.g., methods to process input solutions).
U.S. PGPUB 2023/0259800 issued to Alkan et al. on 17 August 2023. The subject matter disclosed therein is pertinent to that of claims 1-20 (e.g., methods to process patient sensor data).
U.S. PGPUB 2019/0347831 issued to Vardhan on 14 November 2019. The subject matter disclosed therein is pertinent to that of claims 1-20 (e.g., methods to process patient sensor data).
Contact Information
20. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mahesh Dwivedi whose telephone number is (571) 272-2731. The examiner can normally be reached on Monday to Friday 8:20 am – 4:40 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached (571) 272-4085. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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Mahesh Dwivedi
Primary Examiner
Art Unit 2168
July 21, 2026
/MAHESH H DWIVEDI/Primary Examiner, Art Unit 2168
Prosecution Insights