DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This action is made non-final.
Claims 1–7, and 9-20 are pending in the case. Claims 1, 9, and 16 are independent claims. Claim 8 is cancelled.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in China on 08/24/2020. It is noted, however, that applicant has not filed a certified copy of the CN202010856845.6 application as required by 37 CFR 1.55.
Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Specification
The abstract of the disclosure is objected to because it is merely reciting the language of claim 1, and is not a concise statement of the technical disclosure of the patent. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4, 6, 12, 14, 18, and 19 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.
Claims 4 recites the limitation "global index" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 6 recites the limitation "global index" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 12 recites the limitation "global index" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 14 recites the limitation "global index" in line 5. There is insufficient antecedent basis for this limitation in the claim.
Claim 18 recites the limitation "global index" in line 7. There is insufficient antecedent basis for this limitation in the claim.
Claim 19 recites the limitation "global index" in line 5. There is insufficient antecedent basis for this limitation in the claim.
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.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
Step 1: Determining if the claim falls within a statutory category.
Step 2A: Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and Step 2A is a two prong inquiry. MPEP 2106.04(II)(A). Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2104.04(a)(2). The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 2B: If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106).
Claims 1-7, and 9-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-7 are directed to a method (a process), Claim 9-15 are directed to a computing device comprising a memory and one or more processors (a machine), and Claims 16-20 are directed to a computer storage media (a manufacture). Therefore, Claims 1-7, and 9-20 are directed to a process, machine or manufacture or composition of matter.
Regarding claim 1
Step 2A Prong 1
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “sorting model”) [see MPEP 2106.04(a)(2)(III)].
“extracting an attribute data set of multiple dimensions corresponding to the product information” (e.g., a human can identify and extract key values from a dataset containing many rows of data corresponding to products)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of a “sorting model” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). The Examiner notes that this is used throughout the claim limitations, and is rejected thusly for each claim which recites the same language.
Regarding the “acquiring product information” and “inputting the attribute data set of the multiple dimensions into a pre-trained sorting model” limitations, these additional elements are recited at a high-level of generality and amount to extra-solution activity of obtaining data and inputting the data for a model, i.e., pre-solution activity of data gathering for processing in a computer system (see MPEP 2106.05(g)).
Regarding the “identifying the attribute data set of each dimension by using the sorting model until sorting results corresponding to the multiple dimensions are outputted according to a preset number of attribute dimensions” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of outputting a desired data table size, i.e., post-solution activity of data outputting (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of a “sorting model”, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “acquiring product information” and “inputting the attribute data set of the multiple dimensions into a pre-trained sorting model” limitations, as discussed above, these additional elements are recited at a high level of generality and amounts to extra-solution activity of data gathering and manipulation, for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (See MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “identifying the attribute data set of each dimension by using the sorting model until sorting results corresponding to the multiple dimensions are outputted according to a preset number of attribute dimensions” limitation, as discussed above, this additional element is recited at a high level of generality and amounts to extra-solution activity of data outputting for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (See MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Regarding claim 2
Step 2A Prong 1
Claim 2 inherits the same abstract ideas as claim1, and further recites the following mathematical concepts, that in each case under the broadest reasonable interpretation, involves mathematical relationships, formulas, calculations, or algorithms implemented using generic computer components (e.g., “sorting model”, “encoder”, “decoder”) [see MPEP 2106.04(a)(2)(I)].
“calculating a clustering center corresponding to each category of attribute data in the attribute data set through an encoder to obtain corresponding to-be-selected dimension attribute data” (e.g., grouping items by category and compute each category’s average to identify a feature candidate)
“calculating probabilities of the to-be-selected dimension attribute data by using an attention mechanism” (e.g., giving each candidate a score and use that score to calculate a probability of its value)
“setting the probability corresponding to the target dimension attribute data to zero” (e.g., after a candidate is selected, set that candidates probability to zero)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “encoder”, and “decoder” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “selecting target dimension attribute data corresponding to the maximum probability from the to-be-selected dimension attribute data as input data of a decoder limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data and inputting the data for a model, i.e., pre-solution activity of data gathering for processing in a computer system (see MPEP 2106.05(g)).
Regarding the “inputting the target dimension attribute data to the decoder” limitation, this additional element is recited at a high-level of generality of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components as it is processing refined data in the system(See MPEP 2106.05(f)).
Regarding the “outputting a sorting result of a dimension corresponding to the target dimension attribute data” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of outputting a desired data table size, i.e., post-solution activity of data outputting (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “encoder”, and “decoder” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “selecting target dimension attribute data corresponding to the maximum probability from the to-be-selected dimension attribute data as input data of a decoder limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data and inputting the data for a model. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (See MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “inputting the target dimension attribute data to the decoder” limitation, this additional element is recited at a high-level of generality of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components as it is processing refined data in the system(See MPEP 2106.05(f)).
Regarding the “outputting a sorting result of a dimension corresponding to the target dimension attribute data” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of outputting a desired data table size. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (See MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 3
Step 2A Prong 1
Claim 3 inherits the same abstract ideas as its parent claims, and further recites the following mathematical concepts, that in each case under the broadest reasonable interpretation, involves mathematical relationships, formulas, calculations, or algorithms implemented using generic computer components (e.g., “sorting model”, “encoder”, “decoder”) [see MPEP 2106.04(a)(2)(I)].
“calculating a valid probability graph corresponding to the attribute data set of the multiple dimensions by using the attention mechanism” (e.g., grouping items by category and compute each category’s average to identify a feature candidate)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “encoder”, and “decoder” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “ordinates of the probability graph being configured to represent probabilities”, and “abscissas of the probability graph being configured to represent dimensions” limitations, these additional elements are recited at a high-level of generality and amount to extra-solution activity of detailing how information is displayed, i.e., post-solution activity of selecting a particular data source or type of data to be manipulated in use for the claimed method (see MPEP 2106.05(g)).
Regarding the “selecting target dimension attribute data corresponding to the ordinate with the maximum probability in the probability graph as the input data of the decoder” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of inputting transformed data for further processing, i.e., post-solution activity of data gathering (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “encoder”, and “decoder” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “ordinates of the probability graph being configured to represent probabilities”, and “abscissas of the probability graph being configured to represent dimensions” limitations, these additional elements are recited at a high-level of generality and amount to extra-solution activity of displaying data points on a graph. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (See MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “selecting target dimension attribute data corresponding to the ordinate with the maximum probability in the probability graph as the input data of the decoder” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of outputting data from a first run, graphing it, then selecting the highest data probability for further model inputting. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (See MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 4
Step 2A Prong 1
Claim 4 inherits the same abstract ideas as claim 1, and further recites the following mathematical concepts, that in each case under the broadest reasonable interpretation, involves mathematical relationships, formulas, calculations, or algorithms implemented using generic computer components (e.g., “sorting model”, “encoder”, “decoder”) [see MPEP 2106.04(a)(2)(I)].
“acquiring a first function corresponding to the attribute data sample set, taking the first function as an objective function, and determining a loss value based on the objective function” (e.g., defining a mathematical objective and computing a scalar loss from an objective)
“wherein the first function is calculated and generated based on a predicted distance value outputted by a distance prediction model and is configured to evaluate a global index of a multi- dimensional data set” (e.g., use a learned distance model to estimate distances between items, using those distances in a formula to produce a value)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “adjusting parameters of the initial sorting model according to the loss value for iterative training until the determined loss value reaches a training stop condition and the trained sorting model is obtained” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “adjusting parameters of the initial sorting model according to the loss value for iterative training until the determined loss value reaches a training stop condition and the trained sorting model is obtained” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 5
Step 2A Prong 1
Claim 5 does not introduce any new abstract ideas, but does inherit the same abstract ideas as claim 1.
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the attribute data set comprises a star plot set” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of describing the data set, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated in use for the claimed method (see MPEP 2106.05(g)).
Regarding “the star plot set of the multiple dimensions is inputted into the pre-trained sorting model, and the star plot set of each dimension is identified by using the sorting model until sorting results corresponding to the multiple dimensions of the star plot set are outputted according to the preset number of attribute dimensions”, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the attribute data set comprises a star plot set” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of selecting a data source for manipulation for use in the claim method. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (See MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding “the star plot set of the multiple dimensions is inputted into the pre-trained sorting model, and the star plot set of each dimension is identified by using the sorting model until sorting results corresponding to the multiple dimensions of the star plot set are outputted according to the preset number of attribute dimensions”, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). The examiner notes that this is limiting the data to a star-plot set and then feeding that set into the model which then outputs results. This is well-understood, routine, ad conventional activity regarding models and does not integrate the judicial exception into something more(See MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 6
Claim 6 inherits the same abstract ideas as its parents claims, and further recites the following mathematical concepts, that in each case under the broadest reasonable interpretation, involves mathematical relationships, formulas, calculations, or algorithms implemented using generic computer components (e.g., “sorting model”, “encoder”, “decoder”) [see MPEP 2106.04(a)(2)(I)].
“a loss value is determined based on the objective function” (e.g., computing a scalar from the objective)
“wherein the second function is configured to evaluate a global index of a scatter plot” (e.g., computing a dataset score, determining a value between vs within-cluster separation)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “wherein the attribute data sample set comprises a scatter plot set” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of describing the data set, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated in use for the claimed method (see MPEP 2106.05(g)).
Regarding “the parameters of the initial sorting model are adjusted according to the loss value for iterative training until the training stop condition is met and the trained sorting model is obtained”, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “wherein the attribute data sample set comprises a scatter plot set” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of selecting a data source for manipulation for use in the claim method. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (See MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding “the parameters of the initial sorting model are adjusted according to the loss value for iterative training until the training stop condition is met and the trained sorting model is obtained”, which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 7
Step 2A Prong 1
Claim 7 inherits the same abstract ideas as its parents claims, and further recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “sorting model”) [see MPEP 2106.04(a)(2)(III)].
“comparing the predicted value with the supervised value to obtain a corresponding loss value” (e.g., a human can compare two values to determine if the values of one data set is worth more than the other based on their inherent determined values)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “acquiring sampling point sets corresponding to two attribute data samples in the attribute data sample set” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of gathering data for use in the claimed process (see MPEP 2106.05(g)). It is merely to obtain two data sets for a determination of model processing for later steps.
Regarding the “inputting the sampling point sets into an initial distance prediction model to obtain a corresponding predicted value” and “acquiring a supervised value of a distance between the sampling point sets” limitations, these additional elements are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)). It is taking the sample sets and then using a predetermined model to get a value of that sets value, and then needing a known target value to compare the model output with the known value.
Regarding the “adjusting the parameters of the initial distance prediction model according to the loss value for iterative training until the training stop condition is met and a trained distance prediction model is obtained” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of manipulating data, i.e. post-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process (see MPEP 2106.05(g)). It is merely to obtain the output of a models comparison of the data sets, then adjust the model for continued use.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of a “acquiring sampling point sets corresponding to two attribute data samples in the attribute data sample set” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of receiving data for use in the claim method. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (See MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “inputting the sampling point sets into an initial distance prediction model to obtain a corresponding predicted value” and “acquiring a supervised value of a distance between the sampling point sets” limitations, these additional elements are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “adjusting the parameters of the initial distance prediction model according to the loss value for iterative training until the training stop condition is met and a trained distance prediction model is obtained” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of selecting a data source for manipulation for use in the claim method. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (See MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding Claim 8 (Cancelled)
Regarding claims 9-15
Claims 9-15 further recites a computer device, which corresponds directly to the method steps recited in claims 1-7, respectively, with the addition of instructions and computer-executable instructions which are insufficient to render the claims subject matter eligible for the same reasons described above.
Claim 9 corresponds to claim 1, with the added recitation of computer instructions executing method steps to perform the same abstract method steps of claim 1.
Claim 10 corresponds to claim 2, with the added recitation of computer instructions executing method steps to perform the same abstract method steps of claim 2.
Claim 11 corresponds to claim 3, with the added recitation of computer instructions executing method steps to perform the same abstract method steps of claim 3.
Claim 12 corresponds to claim 4, with the added recitation of computer instructions executing method steps to perform the same abstract method steps of claim 4.
Claim 13 corresponds to claim 5, with the added recitation of computer instructions executing method steps to perform the same abstract method steps of claim 5.
Claim 14 corresponds to claim 6, with the added recitation of computer instructions executing method steps to perform the same abstract method steps of claim 6.
Claim 15 corresponds to claim 7, with the added recitation of computer instructions executing method steps to perform the same abstract method steps of claim 7.
Regarding claims 16-20
Claims 16-20 further recites a computer storage media, which corresponds directly to the method steps recited in claims 1-7, respectively, with the addition of instructions and computer-executable instructions which are insufficient to render the claims subject matter eligible for the same reasons described above.
Claim 16 corresponds to claim 1, with the added recitation of computer instructions executing method steps to perform the same abstract method steps of claim 1.
Claim 17 corresponds to claim 2, with the added recitation of computer instructions executing method steps to perform the same abstract method steps of claim 2.
Claim 18 corresponds to claim 4, with the added recitation of computer instructions executing method steps to perform the same abstract method steps of claim 4.
Claim 19 corresponds to claim 6, with the added recitation of computer instructions executing method steps to perform the same abstract method steps of claim 6.
Claim 20 corresponds to claim 7, with the added recitation of computer instructions executing method steps to perform the same abstract method steps of claim 7.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 9 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain et al. (US 10949907 B1, referred to as Jain), in view of Gui et al.( “AFS: An Attention-Based Mechanism for Supervised Feature Selection”, referred to as Gui), in view of Artero et al. (“Enhanced High Dimensional Data Visualization through Dimension Reduction and Attribute Arrangement”, referred to as Artero).
Regarding claim 1, Jain teaches, a product information visualization processing method, comprising (Col. 2, lines20-54L Describes a method for determining attribute data corresponding to a user inquiry for a reference product. This system corresponds to a product information visualization method as it requires a user to give a brief description of a product and the system uses that to search for related products using ,multiple attributes of multiple products, for a learning system.): acquiring product information (Col. 4, lines 4-7: Describes that the system receives a user selection relating to a target product. This corresponds to acquiring product information as a user defines particular product details for the system to correspond to a particular item for determine the correct item referred to form the user.); extracting an attribute data set of multiple dimensions corresponding to the product information (Col. 10, lines 34-48: Describes that once the system receives the user request for product information, it can contain multiple attribute data per the user request.);
Although Jain taches a product information visualization processing method, comprising: acquiring product information; extracting an attribute data set of multiple dimensions corresponding to the product information. It does not teach inputting the attribute data set of the multiple dimensions into a pre-trained sorting model, and identifying the attribute data set of each dimension by using the sorting model until sorting results corresponding to the multiple dimensions are outputted according to a preset number of attribute dimensions.
Gui teaches, inputting the attribute data set of the multiple dimensions into a pre-trained sorting model (Page 3, AFS Architecture, Notation: Describes that the dataset is multi-dimensional input per item, corresponding to an attribute data set of multiple dimensions; Page 3, Architectural design: Describes an attention module, which ; Page 3, cont. page 4, Attention Module: ; Page 5, Learning model reuse mechanism: Describes “Using the trained model, the saved model weights are initialized to the model parameters portion of AFS, called AFS-R. Since the parameters in the learning module are already converged, only a few tunes are needed. There are 2 ways to train the AFS-R: fine-tuning the both attention module and learning module (denoted as AFS-RGlobalTune) or fixing the learning module and only train the attention module (denoted as AFS-R-LocalTune)” These describe that for each single course/item, a column vector xi of d features (i.e., an attribute set of multiple dimensions) and feeds that into a trained model whose attention module computes weights for all features yielding an immediate feature ranking/sorting signal. This reuses previously trained modules (pre-trained) via the AFS-R with saved model weights.)
It would have been to one of ordinary skill at the time of the claimed invention to combine the product attribute data system of Jain with the pre-trained sorting model of Gui. Doing so would allow a system to have a predetermined model to consume less time and resources to search and generate proper product identification.
Although Jain, in view of Gui teaches a product information visualization processing method, comprising: acquiring product information; extracting an attribute data set of multiple dimensions corresponding to the product information; and inputting the attribute data set of the multiple dimensions into a pre-trained sorting model. It does not teach identifying the attribute data set of each dimension by using the sorting model until sorting results corresponding to the multiple dimensions are outputted according to a preset number of attribute dimensions.
Artero teaches, identifying the attribute data set of each dimension by using the sorting model until sorting results corresponding to the multiple dimensions are outputted according to a preset number of attribute dimensions (Page 2 Section 2: “In Sequential Backward Selection (SBS), attributes are gradually removed from the data base, following a specific criterion, until the desired number of attributes is reached.” Describes that a preset attribute dimension is identified prior to running of the model. ;Page 4 Algorithm 2 Box: The sorting/ordering of dimensions which is done iteratively and identifies which dimensions remain in the selected set by repeatedly removing the worst according to that sorting/criterion until a preset number of dimensions is reached.).
It would have been to one of ordinary skill at the time of the claimed invention to combine the product attribute data system of Jain with the pre-trained sorting model of Gui and the counting steps of Artero. Doing so would allow the system to have a predetermined criteria with a desired attribute set of dimensions, allowing for the system to identify key attributes and not over burden itself with unnecessary information.
Regarding claim 9, which recites substantially the same limitations as claim 1. Claim 9 further recites a computer device(Jain, Col. 4, lines 50-60: Describes a computing device with storage devices and processors.) to perform the method steps of claim 1, and is therefore rejected on the same premise
Regarding claim 16, which recites substantially the same limitations as claim 1. Claim 9 further recites a computer storage media(Jain, Col. 4, lines 14-20: Describes a non-transitory computer-readable storage medium.) to perform the method steps of claim 1, and is therefore rejected on the same premise
Claim(s) 2, 3, 10, 11, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain, in view of Gui, in view of Artero, in view of Guo et al. (“Improved Deep Embedded Clustering with Local Structure Preservation”, referred to as Guo).
Regarding claim 2, Jain, in view of Gui, in view of Artero, teaches, the method according to claim 1.
Although Jain in view of Gui, in view of Artero teaches the method of claim 1, they do not teach calculating a clustering center corresponding to each category of attribute data in the attribute data set through an encoder to obtain corresponding to-be-selected dimension attribute data.
Guo teaches, wherein the identifying the attribute data set of each dimension by using the sorting model comprises: calculating a clustering center corresponding to each category of attribute data in the attribute data set through an encoder to obtain corresponding to-be-selected dimension attribute data (Page 2, Section 2.3: Describes using an encoder fw to embed each multi-dimensional record xi into zi and derives/learns cluster centers {ui} in that encoded space, initialized form, k-means on zi and then updated during training. Corresponding to an encoder calculating a clustering center, the resulting encoded representations zi paired with their nearest/similar cluster centers provide to-be-selected target signal for further use.)
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to combine Guo’s encoder-decoder pipeline, with Gui’s feature probability selection. Doing so would enable the system to reuse off-the-shelf networks with lower computation overhead with probability-based feature selection to reduce dimensionality and noise.
Jain, in view of Gui, in view of Artero, in view of Guo, further teaches, calculating probabilities of the to-be-selected dimension attribute data by using an attention mechanism (Gui, FIG. 1, Page 3 cont. Page 4, Attention Module: The attention module takes the multi-dimensional feature vector and outputs, for each dimension, a probability of selection (0-1 via softmax). The attention module acts as the attention mechanism for the attribute data set probabilities.), and selecting target dimension attribute data corresponding to the maximum probability from the to-be-selected dimension attribute data as input data of a decoder (Gui, Page 3, cont. Page 4, Attention Module: Computes a probability of selection per dimension, choosing the maximum (top-k) which is selecting this probability.; Page 4, Learning Module: the selected/weighted features become the input for downstream network use functionally this module takes the selected features and produces task outputs for use in the later steps.); and inputting the target dimension attribute data to the decoder (Gui, Page 4, Learning module: Describes that after the attention module selects the dimensions, those selected features are fed into a downstream network, Although it does not integrate a decoder, combining it with IDEC would have been obvious, the examiner notes this is a common method to use), outputting a sorting result of a dimension corresponding to the target dimension attribute data (Gui, Page 5, Experiment Settings, Evaluation Protocols: “The feature weights are obtained through the training data, and then they are sorted, and a certain number of features are elected as a feature subset in descending order” the attention procedures per-dimension weights, then sorts the, to output the ranked (sorted) result that corresponds to the target (highest probability) dimension(s).), and setting the probability corresponding to the target dimension attribute data to zero (Artero, Page 4, Section 4, Box 3 Algorithm mSBAR: After selecting the current best (highest-probability) dimension, the standard greedy control is to exclude it from further consideration, corresponding to zeroing its probability or ignore it on the next pass.).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to combine the product attribute data system of Jain with the pre-trained sorting model of Gui and the counting steps of Artero, with Gui’s feature probability selection and Guo’s encoder-decoder pipeline. Doing so would enable the system to reuse off-the-shelf networks with lower computation overhead with probability-based feature selection to reduce dimensionality and noise.
Regarding claim 3, Jain, in view of Gui, in view of Artero, in view of Guo teaches, the method according to claim 2. Gui further teaches, wherein the calculating probabilities of the to-be- selected dimension attribute data by using an attention mechanism, and selecting target dimension attribute data corresponding to the maximum probability from the to-be-selected dimension attribute data as input data of a decoder comprises: calculating a valid probability graph corresponding to the attribute data set of the multiple dimensions by using the attention mechanism; ordinates of the probability graph being configured to represent probabilities, and abscissas of the probability graph being configured to represent dimensions; and selecting target dimension attribute data corresponding to the ordinate with the maximum probability in the probability graph as the input data of the decoder(Page 3 cont. Page 4, Attention module: Describes an attention matrix and per-feature weight sk (one probability value per dimension k) the sk values are per-dimension probabilities, the dimension index k is the other axis. Plotting them as the probability graph. ; Page 5, Experiment Settings, Evaluation Protocols: “The feature weights are obtained through the training data, and then they are sorted, and a certain number of features are selected as a feature subset in descending order.” This is selecting the feature at the maximum ordinate in that graph which is picking the arg-max of those per-dimension probabilities/weights.).
Regarding claim 10, which recites substantially the same limitations as claim 2. Claim 10 further recites a computer device(Jain, Col. 4, lines 50-60: Describes a computing device with storage devices and processors.) to perform the method steps of claim 2, and is therefore rejected on the same premise
Regarding claim 11, which recites substantially the same limitations as claim 3. Claim 11 further recites a computer device(Jain, Col. 4, lines 50-60: Describes a computing device with storage devices and processors.) to perform the method steps of claim 3, and is therefore rejected on the same premise
Regarding claim 17, which recites substantially the same limitations as claim 2. Claim 17 further recites a computer storage media(Jain, Col. 4, lines 14-20: Describes a non-transitory computer-readable storage medium.) to perform the method steps of claim 2, and is therefore rejected on the same premise
Claim(s) 4-7, 12-15, 18, 19, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Jain, in view of Gui, in view of Artero, in view of Guo, in view of Caro et al. (“Analyzing the Role of Dimension Arrangement for Data Visualization in Radviz”, referred to as Caro).
Regarding claim 4, Jain, in view of Gui, in view of Artero, teaches, the method according to claim 1. Jain, in view of Gui, in view of Artero, in view of Guo further teaches, wherein a manner of training the sorting model comprises: inputting an attribute data sample set into an initial sorting model (Gui, Page 3, AFS Architecture, Notation: Describes that the dataset is multi-dimensional input per item, corresponding to a attribute data set of multiple dimensions; Page 3, Architectural design: Describes an attention module, which ; Page 3, cont. page 4, Attention Module: ; Page 5, Learning model reuse mechanism: Describes “Using the trained model, the saved model weights are initialized to the model parameters portion of AFS, called AFS-R. Since the parameters in the learning module are already converged, only a few tunes are needed. There are 2 ways to train the AFS-R: fine-tuning the both attention module and learning module (denoted as AFS-RGlobalTune) or fixing