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 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 significantly more. The limitations, under their broadest reasonable interpretation, cover mental process i.e. abstract idea (concept performed in a human mind, including as observation, evaluation, judgment, opinion, organizing human activity and solving mathematically relationship). This judicial exception is not integrated into a practical application because the steps do not add meaningful limitations to be considered specifically applied to a particular technological problem to be solved .The claims 1-17 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the steps of the claimed invention can be done mentally using paper/pencil, solving mathematically relationship using collected data and no additional features in the claims would preclude them from being performed as such except for the generic computer elements at high level of generality (i.e., processor, memory) .
According to the USPTO guidelines, a claim is directed to non-statutory subject matter if:
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis:
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon?
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Using the two-step inquiry, it is clear from claims 1-17, that claims 1-17 are directed toward an abstract idea as shown below:
Regarding independent claims 1, 12, 14 and 15
STEP 1: Do the claims fall within one of the statutory categories?
YES.
Claim(s) 1, 12, 14 and 15 are directed to a learning apparatus and a prediction apparatus i.e. system/ machine
STEP 2A (PRONG 1): Is the claim directed to a law of nature, a natural phenomenon or an abstract idea?
YES.
The claims are directed toward a mental process, human activity and solving mathematical relationship (i.e. abstract idea).
With regard to STEP 2A (PRONG 1), the guidelines provide three groupings of subject matter that are considered abstract ideas:
Mathematical concepts – mathematical relationships, mathematical formulas or equations, mathematical calculations;
Certain methods of organizing human activity – fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); and
Mental processes – concepts that are practicably performed in the human mind (including an observation, evaluation, judgment, opinion).
Claims 1, 12, 14 and 15 comprise a mental process that can be practicably performed in the human mind and solving mathematical relationship (or generic computers or components configured to perform the process) and, therefore, an abstract idea.
Regarding claims 1 and 12 (representative claim 12):
A learning apparatus, comprising:
a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute (generic computing hardware/software components/elements):
an acquisition process of acquiring correct data pertaining to sale of an image data group from a server as a result of transmitting the image data group to the server (collecting correct image data pertaining to sale of an image data group from a server as a result of transmitting the image data group to the server which is insignificant extra solution activity); and
a generation process of generating a learning model that predicts an ease of selling the image data on the basis of the image data group and the correct data acquired during the acquisition process (generating a mathematical function or relationship using paper/pencil related to collected correct image data and to predict ease of selling image data based on monitoring i.e., mental process of solving mathematical relationship and human activity of monitoring sells data) .
Regarding claims 14 and 15 (representative claim 15):
A prediction apparatus, comprising:
a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute (generic computing hardware/software components/elements):
an acquisition process of acquiring a correct data/learning model that predicts an ease of selling the image data (collecting correct image data and mathematical function which is insignificant extra solution activity to predict ease of selling collected image data based on monitoring of sales i.e., human activity and solving mathematical function or relation- ship); and
a prediction process of inputting to-be-predicted image data to the learning model acquired by the acquisition process, thereby generating a score indicating the ease of selling the to-be-predicted image data (predicting using mathematical function or relationship between collected image and monitored sells data and generating a score on paper/pencil based on mathematical- function ease of selling image data ).
The above limitations, as drafted, is a simple process that, under their broadest reasonable interpretation, covers performance of the limitations in the mind or by a human intelligence and solving mathematical problem. Furthermore limitations, “a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute (generic computing hardware/software components/elements), an acquisition process of acquiring correct data pertaining to sale of an image data group from a server as a result of transmitting the image data group to the server (collecting correct image data pertaining to sale of an image data group from a server as a result of transmitting the image data group to the server which is insignificant extra solution activity); and a generation process of generating a learning model that predicts an ease of selling the image data on the basis of the image data group and the correct data acquired during the acquisition process (generating a mathematical function or relationship using paper/pencil related to collected correct image data and to predict ease of selling image data based on monitoring i.e., mental process of solving mathematical relationship and human activity of monitoring sells data)” and “ an acquisition process of acquiring a correct data/learning model that predicts an ease of selling the image data (collecting correct image data and mathematical function which is insignificant extra solution activity to predict ease of selling collected image data based on monitoring of sales i.e., human activity and solving mathematical function or relation- ship) and a prediction process of inputting to-be-predicted image data to the learning model acquired by the acquisition process, thereby generating a score indicating the ease of selling the to-be-predicted image data (predicting using mathematical function or relationship between collected image and monitored sells data and generating a score on paper/pencil based on mathematical- function ease of selling image data )” are insignificant.
The Examiner notes that under MPEP 2106.04(A) (2) (III), the courts consider a mental process (thinking, human intelligence) that can be performed in the mind/intelligence using a paper and pencil to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[Mental processes and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978).
Furthermore the Examiner also notes that even if you combined the math with the mental process, a combination of abstract ideas don't make a claim eligible. See MPEP 2106.04(II)(A)(2): Because a judicial exception is not eligible subject matter, Bilski, 561 U.S. at 601, 95 USPQ2d at 1005-06 (quoting Chakrabarty, 447 U.S. at 309, 206 USPQ at 197 (1980)), if there are no additional claim elements besides the judicial exception, or if the additional claim elements merely recite another judicial exception, that is insufficient to integrate the judicial exception into a practical application. See, e.g., RecogniCorp, LLC v. Nintendo Co., 855 F.3d 1322, 1327, 122 USPQ2d 1377 (Fed. Cir. 2017) ("Adding one abstract idea (math) to another abstract idea (encoding and decoding) does not render the claim non-abstract").
Other than generic and well-known computer components/elements recited in the independent claims 1, 12, 14 and 15 i.e. generic computer hardware/software as disclosed in the specification, nothing in the claims 1, 12. 14 and 15 elements preclude the processing from being performed as mental process, including as observation, evaluation, judgment, opinion, organizing human activity and solving mathematical problem solving. A generation process of generating a learning model that predicts an ease of selling the image data on the basis of the image data group and the correct data acquired during the acquisition process and “A prediction process of inputting to-be-predicted image data to the learning model acquired by the acquisition process, thereby generating a score indicating the ease of selling the to-be-predicted image data” as recited in independent claims 1, 12 14 and 15 is a mere idea of a solution without details per MPEP 2106.05( f ) or the idea of a technological environment without detail per MPEP 2106.05 ( h ). The generic computing hardware/software and learning/prediction models are recited in the claims as just to automate the mental process of observation, judgement, human activity and mathematical problem solving (Step 2A, prong 1 Test Abstract idea = Yes).
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application?
[YES/NO].
The claims do not recite additional elements that integrate the judicial exception into a practical application.
With regard to STEP 2A (prong 2), whether the claim recites additional elements that integrate the judicial exception into a practical application, the guidelines provide the following exemplary considerations that are indicative that an additional element (or combination of elements) may have integrated the judicial exception into a practical application:
an additional element reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field;
an additional element that applies or uses a judicial exception to affect a particular treatment or prophylaxis for a disease or medical condition;
an additional element implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim;
an additional element effects a transformation or reduction of a particular article to a different state or thing; and
an additional element applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
While the guidelines further state that the exemplary considerations are not an exhaustive list and that there may be other examples of integrating the exception into a practical application, the guidelines also list examples in which a judicial exception has not been integrated into a practical application:
an additional element merely recites the words “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea;
an additional element adds insignificant extra-solution activity to the judicial exception; and
an additional element does no more than generally link the use of a judicial exception to a particular technological environment or field of use.
Claim(s) 1, 12, 14 and 15 do not recite any of the exemplary considerations that are indicative of an abstract idea having been integrated into a practical application. Claim(s) 1, 12, 14 and 15 recite(s) the limitations of:
Regarding claims 1 and 12 (representative claim 12):
A learning apparatus, comprising:
a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute (generic computing hardware/software components/elements):
an acquisition process of acquiring correct data pertaining to sale of an image data group from a server as a result of transmitting the image data group to the server (collecting correct image data pertaining to sale of an image data group from a server as a result of transmitting the image data group to the server which is insignificant extra solution activity); and
a generation process of generating a learning model that predicts an ease of selling the image data on the basis of the image data group and the correct data acquired during the acquisition process (generating a mathematical function or relationship using paper/pencil related to collected correct image data and to predict ease of selling image data based on monitoring i.e., mental process of solving mathematical relationship and human activity of monitoring sells data) .
Regarding claims 14 and 15 (representative claim 15):
A prediction apparatus, comprising:
a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute (generic computing hardware/software components/elements):
an acquisition process of acquiring a correct data/learning model that predicts an ease of selling the image data (collecting correct image data and mathematical function which is insignificant extra solution activity to predict ease of selling collected image data based on monitoring of sales i.e., human activity and solving mathematical function or relation- ship); and
a prediction process of inputting to-be-predicted image data to the learning model acquired by the acquisition process, thereby generating a score indicating the ease of selling the to-be-predicted image data (predicting using mathematical function or relationship between collected image and monitored sells data and generating a score on paper/pencil based on mathematical- function ease of selling image data ).
These limitations are recited at a high level of generality (i.e. as general action or calculation being taken based on the results of the acquiring steps) and amounts to mere post solution actions, which is a form of insignificant extra-solution activity without further detail. Furthermore, claims 1, 12, 14 and 15 are claimed generically and are operating in their ordinary capacity such that they do not use the judicial exception in a manner that imposes a meaningful limitation on the judicial exception. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limitations on practicing the abstract idea.
Other than generic and well-known computer components/elements recited in the independent claims 1, 12, 14 and 15 i.e. generic computer hardware/software as disclosed in the specification, nothing in the claims 1, 12. 14 and 15 elements preclude the processing from being performed as mental process, including as observation, evaluation, judgment, opinion, organizing human activity and solving mathematical problem solving. A generation process of generating a learning model that predicts an ease of selling the image data on the basis of the image data group and the correct data acquired during the acquisition process and “A prediction process of inputting to-be-predicted image data to the learning model acquired by the acquisition process, thereby generating a score indicating the ease of selling the to-be-predicted image data” as recited in independent claims 1, 12 14 and 15 is a mere idea of a solution without details per MPEP 2106.05( f ) or the idea of a technological environment without detail per MPEP 2106.05 ( h ). The generic computing hardware/software and learning/prediction models are recited in the claims as just to automate the mental process of observation, judgement, human activity and mathematical problem solving (Step 2A, prong 2 Test Abstract idea = Yes).
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
NO.
Claims 1, 12, 14 and 15 do not recite additional elements that amount to significantly more than the judicial exception.
With regard to STEP 2B, whether the claims recite additional elements that provide significantly more than the recited judicial exception, the guidelines specify that the pre-guideline procedure is still in effect. Specifically examiners should continue to consider whether an additional element or combination of elements:
adds a specific limitation or combination of limitations that are not well-understood, routine, conventional activity in the field, which is indicative that an inventive concept may be present; or
simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, which is indicative that an inventive concept may not be present.
As noted above other than generic and well-known computer components/elements recited in the independent claims 1, 12, 14 and 15 i.e. generic computer hardware/software as disclosed in the specification, nothing in the claims 1, 12. 14 and 15, elements preclude the processing from being performed as mental process, including as observation, evaluation, judgment, opinion, organizing human activity and solving mathematical problem solving. A generation process of generating a learning model that predicts an ease of selling the image data on the basis of the image data group and the correct data acquired during the acquisition process” and “A prediction process of inputting to-be-predicted image data to the learning model acquired by the acquisition process, thereby generating a score indicating the ease of selling the to-be-predicted image data” as recited in independent claims 1, 12 14 and 15 are a mere idea of a solution without details per MPEP 2106.05( f ) or the idea of a technological environment without detail per MPEP 2106.05 ( h ). The generic computing hardware/software and learning/prediction models are recited in the claims as just to automate the mental process of observation, judgement, human activity and mathematical problem solving.
Thus, since Claim(s) 1, 12, 14 and 15 are: (a) directed toward an abstract idea, (b) do not recite additional elements that integrate the judicial exception into a practical application, and (c) do not recite additional elements that amount to significantly more than the judicial exception, it is clear that Claim(s) 1, 12, 14 and 15 are not eligible subject matter under 35 U.S.C 101 (Step 2B, Test Abstract idea = Yes).
Regarding dependent claims 2-11, 13 and 16-17: the additional limitations of dependent claims 2-11, 13 and 16-17 , do not integrate the mental process into practical application or add significantly more to the abstract idea of mental process of observation, judgement, prediction, human activity and solving mathematical relationship. Claims 2-11, 13 and 16-17 further limit the abstract idea of independent claims 1, 12, 14 and 15. The limitations of these dependent claims fall under (mental process including observation, evaluation, prediction, human activity and mathematical problem solving of mathematical relation-ship which can be done mentally in the human mind based on human intelligence using paper and pencil OR (insignificant pre/post-solution extra activity of generating/gathering data), performing mathematical calculation) OR (generic computers or components (modules) configured to perform the process and learning/prediction process). “a generation process of generating a learning model that predicts an ease of selling the image data on the basis of the image data group and the correct data acquired during the acquisition process” and “a prediction process of inputting to-be-predicted image data to the learning model acquired by the acquisition process, thereby generating a score indicating the ease of selling the to-be-predicted image data” as recited in independent claims 1, 12 14 and 15 are a mere idea of a solution without details per MPEP 2106.05( f ) or the idea of a technological environment without detail per MPEP 2106.05 ( h ). The generic computing hardware/software and learning/prediction models are recited in the claims as just to automate the mental process of observation, judgement, human activity and mathematical problem solving.
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 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.
Claims 1-17 are rejected under 35 U.S.C. 103 as being unpatentable over Ma et al (Understanding Image Quality and Trust in Peer-to-Peer Marketplaces, arXiv:1811.10648v1 [cs.CV]. 26 Nov 2018, pages 1-10, USPTO-892) in view of Agarwal et al. (US 20140180758)
Regarding claims 1 and 12 Ma discloses a learning apparatus (Ma Abstract, Fig. 1, page 1, disclose We study the interplay between image quality, marketplace outcomes, and user trust in peer-to-peer online marketplaces. Here, we show how image quality (as measured by a deep-learned CNN model) correlates with user trust. User studies in Sec. 6.2 show that high quality images selected by our model outperform stock-imagery in eliciting user trust and page 8, right-column, section 7 Conclusion, Ma states, this work attempted to develop a deeper understanding of image quality in online marketplaces through a computational approach. By gathering and annotating a large-scale dataset of photos from online marketplaces, we were able to develop a deeper understanding of the visual factors that improve image quality, while reaching a decent accuracy (≈87%) for predicting image quality. We have also demonstrated how predicted image quality is useful in the study of online marketplaces — especially for trust. High quality images selected by our model outperform stock imagery in earning the trust of a potential buyer. This obviously corresponds to learning system), comprising:
a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute (Ma, Fig. 1 online-market places and deep learned CNN model and page 8, section 7 Conclusion, develop a deeper understanding of image quality in online marketplaces through a computational approach obviously include a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute):
an acquisition process of acquiring correct data pertaining to sale of an image data group from a server as a result of transmitting the image data group to the server (Ma, Fig. 1, page 3, sections 3.1-3.2 LetGo.com and eBay left column disclose We collected product images data for two product categories, shoes and handbags. We crawled the front page of LetGo.com every ten to thirty minutes for a month, filtering the listings by relevant keywords in the product listing caption. For shoes, we used the keywords “shoe,” “sandal and We collected data for listings on eBay in our two product categories, shoes and handbags, including the product images, meta-data associated with the listing, as well as whether the listing had at least one sales completed before becoming expired and page 5, right column, section 5.1.3 Regression Analysis, Ma discloses after calculating the image features, we analyzed their impact on image quality through multiple ordered logistic regression. Our dependent variable is the three image quality labels (bad, neutral, good) annotated through the crowdsourcing task. This obviously corresponds to an acquisition process of acquiring correct data pertaining to sale of an image data group from a server as a result of transmitting the image data group to the server); and
a generation process of generating a learning model that predicts an ease of selling the image data on the basis of the image data group and the correct data acquired during the acquisition process (Ma Abstract, Fig. 1, page 1, disclose We study the interplay between image quality, marketplace outcomes, and user trust in peer-to-peer online marketplaces. Here, we show how image quality (as measured by a deep-learned CNN model) correlates with user trust. User studies in Sec. 6.2 show that high quality images selected by our model outperform stock-imagery in eliciting user trust, page 6, right column section 6 discloses We focus on two complementary marketplace outcomes: (1) Sales: whether an individual listing with higher quality photos is more likely to generate sales; and (2) Perceived trustworthiness: whether a market place with higher quality photos is perceived as more trustworthy, page 7, left column section 6.1 Image Quality and Sales, Ma in third paragraph discloses From the regression analysis, we show that image quality predicted by our models is associated with higher likelihood that an item is sold(odds ratio1.17for shoes, 1.25f or handbags. This obviously corresponds to a generation process of generating a learning model that predicts an ease of selling the image data on the basis of the image data group and the correct data acquired during the acquisition process).
In the same field of endeavor Agarwal discloses a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute: (Agarwal, paragraph 0096-0097 and claim 18),
a generation process of generating a learning model that predicts an ease of selling the image data on the basis of the image data group and the correct data acquired during the acquisition process (Agarwal Figs. 1A and 1B blocks 135-137 and 140-141 paragraph 0054 discloses generating learning model and paragraph 0021 disclose evaluating images quality and paragraph 0022 states “a price at which the object sold, a price-sales function, a sales-time on site function, and a human scored quality and price function can be part of the data collected during the learning phase to determine the image quality score of an image and further determine the consequent features of an image that result in an increased interest in the item. Specifically, it may be determined that items that sold for higher prices normally were displayed on the network-based publication system with a higher quality image. For example, the data may show that clothing sells at a higher price when worn by a person rather than simply displayed against a background. This obviously corresponds to a generation process of generating a learning model that predicts an ease of selling the image data on the basis of the image data group and the correct data acquired during the acquisition process)
Therefore, it would be obvious before the filing data of the claimed invention to generate a learning model to predict ease of selling the image data on the basis of the correct image data group as shown by combination of Ma and Agarwal because such a prediction model provides automated system to determine the image quality score of an image on-line and determine an image features which result in an increased interest in the item for sell on-line.
Regarding claims 14 and 15 Ma discloses a prediction apparatus (Ma Abstract, Fig. 1, page 1, disclose We study the interplay between image quality, marketplace outcomes, and user trust in peer-to-peer online marketplaces. Here, we show how image quality (as measured by a deep-learned CNN model) correlates with user trust. User studies in Sec. 6.2 show that high quality images selected by our model outperform stock-imagery in eliciting user trust and page 8, right-column, section 7 Conclusion, Ma states, this work attempted to develop a deeper understanding of image quality in online marketplaces through a computational approach. By gathering and annotating a large-scale dataset of photos from online marketplaces, we were able to develop a deeper understanding of the visual factors that improve image quality, while reaching a decent accuracy (≈87%) for predicting image quality. We have also demonstrated how predicted image quality is useful in the study of online marketplaces — especially for trust. High quality images selected by our model outperform stock imagery in earning the trust of a potential buyer): comprising:
a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute (Ma, Fig. 1 online-market places and deep learned CNN model and page 8, section 7 Conclusion, develop a deeper understanding of image quality in online marketplaces through a computational approach obviously include a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute):
an acquisition process of acquiring a correct data/learning model that predicts an ease of selling the image data (Ma Abstract, Fig. 1, page 1, disclose We study the interplay between image quality, marketplace outcomes, and user trust in peer-to-peer online marketplaces. Here, we show how image quality (as measured by a deep-learned CNN model) correlates with user trust. User studies in Sec. 6.2 show that high quality images selected by our mode and Ma, Fig. 1, page 3, sections 3.1-3.2 LetGo.com and eBay left column disclose We collected product images data for two product categories, shoes and handbags. We crawled the front page of LetGo.com every ten to thirty minutes for a month, filtering the listings by relevant keywords in the product listing caption. For shoes, we used the keywords “shoe,” “sandal and We collected data for listings on eBay in our two product categories, shoes and handbags, including the product images, meta-data associated with the listing, as well as whether the listing had at least one sales completed before becoming expired and page 5, right column, section 5.1.3 Regression Analysis, Ma discloses after calculating the image features, we analyzed their impact on image quality through multiple ordered logistic regression. Our dependent variable is the three image quality labels (bad, neutral, good) annotated through the crowdsourcing task. This obviously corresponds to an acquisition process of acquiring a correct data/learning model that predicts an ease of selling the image data); and
a prediction process of inputting to-be-predicted image data to the learning model acquired by the acquisition process, thereby generating a score indicating the ease of selling the to-be-predicted image data (Ma Abstract, Fig. 1, page 1, disclose We study the interplay between image quality, marketplace outcomes, and user trust in peer-to-peer online marketplaces. Here, we show how image quality (as measured by a deep-learned CNN model) correlates with user trust. User studies in Sec. 6.2 show that high quality images selected by our model outperform stock-imagery in eliciting user trust, page 6, right column section 6 discloses We focus on two complementary marketplace outcomes: (1) Sales: whether an individual listing with higher quality photos is more likely to generate sales; and (2) Perceived trustworthiness: whether a market place with higher quality photos is perceived as more trustworthy, page 7, left column section 6.1 Image Quality and Sales, Ma in third paragraph discloses From the regression analysis, we show that image quality predicted by our models is associated with higher likelihood that an item is sold(odds ratio1.17for shoes, 1.25f or handbags. This obviously corresponds to a prediction process of inputting to-be-predicted image data to the learning model acquired by the acquisition process, thereby generating a score indicating the ease of selling the to-be-predicted image data).
In the same field of endeavor Agarwal discloses a processor that executes a program; and a storage device that stores the program, wherein the processor is configured to execute: (Agarwal, paragraph 0096-0097 and claim 18); and
a prediction process of inputting to-be-predicted image data to the learning model acquired by the acquisition process, thereby generating a score indicating the ease of selling the to-be-predicted image data (Agarwal Figs. 1A and 1B blocks 135-137 and 140-141 paragraph 0054 discloses learning model and paragraph 0021 disclose evaluating images quality and paragraph 0022 states “a price at which the object sold, a price-sales function, a sales-time on site function, and a human scored quality and price function can be part of the data collected during the learning phase to determine the image quality score of an image and further determine the consequent features of an image that result in an increased interest in the item. Specifically, it may be determined that items that sold for higher prices normally were displayed on the network-based publication system with a higher quality image. For example, the data may show that clothing sells at a higher price when worn by a person rather than simply displayed against a background. This obviously corresponds to a prediction process of inputting to-be-predicted image data to the learning model acquired by the acquisition process, thereby generating a score indicating the ease of selling the to-be-predicted image data )
Therefore, it would have been obvious before the filing data of the claimed invention to predict based on learning model a score indicating the ease of selling correct image data group on- line as shown by combination of Ma and Agarwal because such a prediction model provides automated system to determine the image quality score of an image on-line and determine an image features which result in an increased interest in the item for sell on-line.
Regarding claim 2, Ma disclose correct data is correct data pertaining to a purchase count of the image data (Ma Abstract disclose We show that image quality is associated 3 (Predicted) Stock Images with higher likelihood that an item will be sold, though other factors such as view count were better predictors of sales, page 7, left column, section 6.1 Image Quality and sales, third paragraph).
Further Agarwal disclose correct data is correct data pertaining to a purchase count of the image data (Agarwal paragraph 0022)
Regarding claim 3, Ma disclose the correct data is correct data pertaining to view information of the image data (Ma Abstract disclose We show that image quality is associated 3 (Predicted) Stock Images with higher likelihood that an item will be sold, though other factors such as view count were better predictors of sales, page 8, right column section 7 Conclusion).
Further Agarwal discloses the correct data is correct data pertaining to view information of the image data (Agarwal paragraph 0022)
Regarding claim 4 Ma disclose the view information is a view count and/or a view time of the image data (Ma Abstract Ma Abstract disclose We show that image quality is associated 3 (Predicted) Stock Images with higher likelihood that an item will be sold, though other factors such as view count were better predictors of sales, page 8, right column section 7 Conclusion).
Further the view information is a view count and/or a view time of the image data (Agarwal paragraph 0022).
Regarding claim 5 Ma disclose learning model is generated using information pertaining to a subject in the image data (Ma Abstract, Fig. 1, page 1, disclose We study the interplay between image quality, marketplace outcomes, and user trust in peer-to-peer online marketplaces. Here, we show how image quality (as measured by a deep-learned CNN model) correlates with user trust and note: Fig. 1 includes items images to be sold which is subject).
Regarding claim 6, Ma discloses information pertaining to the subject is a position, a pose, and/or a defocus amount of the subject in the image data (Ma page 5, left column Table 1 includes image feature definition and examples Global feature includes resolution i.e. focus/defocus and object features include pose/position objects to be sold).
Regarding claim 7, Ma discloses the information pertaining to the subject is a size of the subject in the image data and a size of another subject and/or a size of a background (Ma page 5, left column Table 1 includes global feature includes size (width/height) Regional feature include foreground/background ratio in terms of pixel size).
Regarding claim 8, Ma disclose the learning model is generated using image feature data of the image data when the image data was captured (Ma page 5, paragraph 5.1.3 page 6 paraph 6, Fig 1 disclose generating model using images and page 5, left column Table shows the list of features and it would be obvious to use the image features when the image data was captured).
Regarding claim 9 Ma disclose prediction process of inputting to-be-predicted image data to the learning model, thereby generating a score indicating the ease of selling the to-be-predicted image data (Ma Abstract, Fig. 1, page 1, disclose We study the interplay between image quality, marketplace outcomes, and user trust in peer-to-peer online marketplaces. Here, we show how image quality (as measured by a deep-learned CNN model) correlates with user trust. User studies in Sec. 6.2 show that high quality images selected by our model outperform stock-imagery in eliciting user trust, page 6, right column section 6 discloses We focus on two complementary marketplace outcomes: (1) Sales: whether an individual listing with higher quality photos is more likely to generate sales; and (2) Perceived trustworthiness: whether a market place with higher quality photos is perceived as more trustworthy, page 7, left column section 6.1 Image Quality and Sales, Ma in third paragraph discloses From the regression analysis, we show that image quality predicted by our models is associated with higher likelihood that an item is sold(odds ratio1.17for shoes, 1.25f or handbags).
Further Agarwal disclose prediction process of inputting to-be-predicted image data to the learning model, thereby generating a score indicating the ease of selling the to-be-predicted image data (Agarwal Figs. 1A and 1B blocks 135-137 and 140-141 paragraph 0054 discloses learning model and paragraph 0021 disclose evaluating images quality and paragraph 0022 states “a price at which the object sold, a price-sales function, a sales-time on site function, and a human scored quality and price function can be part of the data collected during the learning phase to determine the image quality score of an image and further determine the consequent features of an image that result in an increased interest in the item. Specifically, it may be determined that items that sold for higher prices normally were displayed on the network-based publication system with a higher quality image).
Regarding claim 10 Agarwal discloses relearning of the learning model on the basis of the correct data and the image data to which a score indicating the ease of selling with a value exceeding a prescribed threshold is applied, among the image data to which the scores are applied (Agarwal Figs 2-3, paragraphs 0009-0010, disclose images with higher to lower quality score and the system of the Agarwal paragraph 0022 disclose learning model i.e., “a price at which the object sold, a price-sales function, a sales-time on site function, and a human scored quality and price function can be part of the data collected during the learning phase to determine the image quality score of an image and further determine the consequent features of an image that result in an increased interest in the item. Specifically, it may be determined that items that sold for higher prices normally were displayed on the network-based publication system with a higher quality image and also paragraph 0023”. In the system of Agarwal it would be obvious to relearn or retrain the learning model on the basis of the correct data and the image data to which a score indicating the ease of selling with a value exceeding a prescribed threshold is applied, among the image data to which the scores are applied).
Regarding claim 11 Agarwal disclose display the image data to which the scores were applied in order of the score, or displays the image data to which the score having a value exceeding a prescribed threshold, among the image data to which the scores were applied, at a higher rank than the image data with the score at or below the prescribed threshold (Agarwal, Figs. 2-3, paragraph 0010, 0022 and 0056 disclose online auction or other network-based publication system can use an embodiment of the image scoring system in several manners, As indicated at 150, the operator can cause the network-based publication system to redisplay the images on the network-based publication system based on the rankings of the images. More specifically, as indicated at 151, images with higher scores can be placed higher up on a search results page. For example, if a potential buyer is looking to purchase a particular item, and the buyer's search returns several of those items on the auction site, the auction operator can place the higher scored images at the top of the search results”. In the system of Agarwal it is obvious to displays the image data to which the score having a value exceeding a prescribed threshold, among the image data to which the scores were applied, at a higher rank than the image data with the score at or below the prescribed threshold and it is obvious that the system of Agarwal include threshold to distinguish higher and lower score).
Regarding claim 13 Ma disclose prediction process of inputting to-be-predicted image data to the learning model, thereby generating a score indicating the ease of selling the to-be-predicted image data (Ma Abstract, Fig. 1, page 1, disclose We study the interplay between image quality, marketplace outcomes, and user trust in peer-to-peer online marketplaces. Here, we show how image quality (as measured by a deep-learned CNN model) correlates with user trust. User studies in Sec. 6.2 show that high quality images selected by our model outperform stock-imagery in eliciting user trust, page 6, right column section 6 discloses We focus on two complementary marketplace outcomes: (1) Sales: whether an individual listing with higher quality photos is more likely to generate sales; and (2) Perceived trustworthiness: whether a market place with higher quality photos is perceived as more trustworthy, page 7, left column section 6.1 Image Quality and Sales, Ma in third paragraph discloses From the regression analysis, we show that image quality predicted by our models is associated with higher likelihood that an item is sold(odds ratio1.17for shoes, 1.25f or handbags).
Further Agarwal discloses prediction process of inputting to-be-predicted image data to the learning model, thereby generating a score indicating the ease of selling the to-be-predicted image data (Agarwal Figs. 1A and 1B blocks 135-137 and 140-141 paragraph 0054 discloses learning model and paragraph 0021 disclose evaluating images quality and paragraph 0022 states “a price at which the object sold, a price-sales function, a sales-time on site function, and a human scored quality and price function can be part of the data collected during the learning phase to determine the image quality score of an image and further determine the consequent features of an image that result in an increased interest in the item. Specifically, it may be determined that items that sold for higher prices normally were displayed on the network-based publication system with a higher quality image).
Regarding claim 16 Agarwal discloses determining whether to transmit the to-be-predicted image data on the basis of the score generated by the prediction process; and a transmission process of transmitting the be-predicted image data on the basis of a determination result by the determination process (Agarwal. Figs. 1B and 2, paragraphs 0018 and 0056 disclose “An operator of an online auction or other network-based publication system can use an embodiment of the image scoring system in several manners, As indicated at 150, the operator can cause the network-based publication system to redisplay the images on the network-based publication system based on the rankings of the images. More specifically, as indicated at 151, images with higher scores can be placed higher up on a search results page. For example, if a potential buyer is looking to purchase a particular item, and the buyer's search returns several of those items on the auction site, the auction operator can place the higher scored images at the top of the search results page(s), thereby increasing the likelihood that the buyer will actually purchase one of the displayed items”. In the system of Agarwal it would be obvious to auctioneer or system administrator to determine whether to transmit other merchants or auctioneer predicted image data on the basis of the score generated by the prediction process; and a transmission process of transmitting the be-predicted image data on the basis of a determination result by the determination process so they can be informed of the higher quality image which would result in higher price and interest of the customer).
Regarding 17 Agarwal disclose the prediction apparatus and an imaging unit that captures a subject, wherein image data of a subject captured by the imaging unit is inputted to the learning model (Agarwal Fig.1A-1B and 2 paragraph 0022 disclose prediction and learning system i.e., which category of images would be sold at higher prices and paragraph 0012 disclose system for scoring images in connection with displaying the images on a network-based publication system includes a learning phase and an execution phase. In general, the learning phase is illustrated in steps 105-137 of FIG. 1, and the execution phase is illustrated in steps 140-152 of FIG. 1. Referring specifically to FIG. 1, at 105, a computer processor receives images of objects. As indicated at 107, the objects in the images can be identified using the output of a foreground segmentation algorithm. The objects in the images can be items that will be displayed on a network-based publication system. The objects or items can be newly-received objects from potential sellers, images previously displayed on the network-based publication system, and/or images specifically created for display during the learning phase. Therefore Agarwal system obviously include the prediction apparatus and an imaging unit that captures a subject, wherein image data of a subject captured by the imaging unit is inputted to the learning model )
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ISHRAT I. SHERALI
Examiner
Art Unit 2667
/ISHRAT I SHERALI/Primary Examiner, Art Unit 2667
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