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 .
Status of Claims
Claims 1 – 16 are pending and examined herein.
Claims 1 – 16 are rejected under 35 U.S.C. 112(b).
Claims 1 – 16 are rejected under 35 U.S.C. 101.
Claims 1 – 16 are rejected under 35 U.S.C. 103.
Information Disclosure Statement
The information disclosure statement filed 6/15/2023 fails to comply with 37 CFR 1.98(a)(1), which requires the following: (1) a list of all patents, publications, applications, or other information submitted for consideration by the Office; (2) U.S. patents and U.S. patent application publications listed in a section separately from citations of other documents; (3) the application number of the application in which the information disclosure statement is being submitted on each page of the list; (4) a column that provides a blank space next to each document to be considered, for the examiner’s initials; and (5) a heading that clearly indicates that the list is an information disclosure statement. The information disclosure statement has been placed in the application file, but the information referred to therein has not been considered.
Specification
Applicant is reminded of the proper content of an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
If the patent is of a basic nature, the entire technical disclosure may be new in the art, and the abstract should be directed to the entire disclosure. If the patent is in the nature of an improvement in an old apparatus, process, product, or composition, the abstract should include the technical disclosure of the improvement. The abstract should also mention by way of example any preferred modifications or alternatives.
Where applicable, the abstract should include the following: (1) if a machine or apparatus, its organization and operation; (2) if an article, its method of making; (3) if a chemical compound, its identity and use; (4) if a mixture, its ingredients; (5) if a process, the steps.
Extensive mechanical and design details of an apparatus should not be included in the abstract. The abstract should be in narrative form and generally limited to a single paragraph within the range of 50 to 150 words in length.
See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
The abstract of the disclosure is objected to because the abstract merely recites claim language without providing a clear and concise summary of the technical disclosure of the invention. 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 1 - 16 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 15, 16 recites the limitation “appears in at least any mathematical model” in line 4. The limitation is unclear and renders the scope of the claims uncertain. It is not clear whether the claim requires a variable that appears in at least one mathematical model, any one mathematical model, every mathematical model, or some other situation. Also, the limitation “of a plurality of mathematical models among a plurality of variables” in line 5 is unclear. It does not clearly identify the set of variables to which the limitation refers. It is unclear whether the limitation refers to all variables under consideration, some selected variables appearing in all mathematical models, a selected subset of variables, or some other combinations possible. Further, repeated limitation “any variable”, “each variable”, “any mathematical model”, and “each mathematical model” contributes to uncertainty as to whether the limitation refers to specific variable and model sets or all of them or just one of them. For examination purposes, “appears in at least any mathematical model” would refer to “at least one mathematical model”, “among a plurality of variables” would refer to specific set of variables from selected mathematical models, and “any variable/mathematical model” would refer to “at least one of the variable/model”, “each variable/mathematical model” would refer to “all variables/models in the selected set”.
Claim 13 recites the limitation “wherein the mathematical model includes a term…” in line 2. With respect to the claim 1 limitation “a plurality of mathematical models” as the base of the claim 13, it is unclear whether claim 13 requires that each mathematical model includes the recited term, at least one mathematical model includes the recited term, or that some other subset of the plurality of mathematical models include the recited term. For examination purposes, the limitation would be treated as requiring that at least one mathematical model of the plurality of mathematical models includes the recited “term”.
The term “relatively high” in claim 8 and “relatively large” in claim 9 are a relative term which renders the claim indefinite. The term “relatively” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. claim 8 "specified degree of variation" and claim 9 "variables with the specified amount" are rendered indefinite with these terms. For examination purposes, these terms “relatively high/large” would be treated as comparative rankings within the recited set of variables.
Claims 2 – 14 are dependent on claim 1. They do not resolve the issue of indefiniteness and are rejected with the same rationale.
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 - 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject matter. The analysis of claims 1 – 16, in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine, manufacture, or composition of matter.
Claims 1 – 14 are directed to a non-transitory computer readable recording medium, meaning that it is directed to the statutory category of manufacture. Claim 15 is directed to an information processing method implemented by a computer, which is the statutory category of process. Claim 16 is directed to an information processing apparatus, which can be an article of machine.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
Regarding claim 1, the following claim elements are abstract ideas:
acquiring evaluation for any variable (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
specifying a frequency of appearance of each variable of the plurality of variables in each mathematical model of the plurality of mathematical models; (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper. Also, specifying a frequency of appearance of variable could also recite a mathematical calculation, which is mathematical concept.)
determining evaluation for the each mathematical model based on an importance level for the any variable set according to the acquired evaluation and the specified frequency. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing comprising (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
that appears in at least any mathematical model of a plurality of mathematical models among a plurality of variables; (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. Further, claim 2 recites the following abstract ideas:
wherein the determining includes determining the evaluation for the each mathematical model based on the importance level for the any variable set according to the acquired evaluation, an importance level preset for each variable of remaining variables other than the any variable among the plurality of variables, and the specified frequency. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
Claim 2 does not recite additional elements.
Regarding claim 3, the rejection of claim 1 is incorporated herein. Further, claim 3 recites the following additional element:
outputting information regarding the each mathematical model in a mode that corresponds to the evaluation determined for the mathematical model. (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 4, the rejection of claim 1 is incorporated herein. Further, claim 4 recites the following additional element:
wherein information regarding the evaluation determined for the each mathematical model is output. (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 5, the rejection of claim 1 is incorporated herein. Further, claim 5 recites the following abstract ideas:
selecting a variable that has not yet been selected from among the plurality of variables, (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
wherein the acquiring includes acquiring, … , evaluation from a user for the variable selected this time, and (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
the determining includes determining, … ,the evaluation for the each mathematical model based on an importance level set according to the evaluation for the variable for which the evaluation from the user has been acquired among the plurality of variables, and the specified frequency. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
Claim 5 further recites following additional elements:
every time the variable that has not yet been selected is selected from among the plurality of variables (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
every time the evaluation from the user is acquired, (This is mere data gathering, an insignificant extra solution activity, which is a well-understood, routine conventional activity. It does not integrate the judicial exception into a practical application. See MPEP § 2106.05(d). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 6, the rejection of claim 5 is incorporated herein. Further, claim 6 recites the following additional element:
wherein the selecting is executed in a case where a variable that has not yet been selected remains among the plurality of variables. (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 7, the rejection of claim 5 is incorporated herein. Further, claim 7 recites the following additional element:
wherein the selecting is executed in response to an instruction from the user. (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 8, the rejection of claim 5 is incorporated herein. Further, claim 8 recites the following abstract ideas:
specifying a degree of variation in the evaluation for the each mathematical model in a case where the evaluation from the user for the each variable satisfies a predetermined condition, (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
wherein the processing of selecting includes selecting a variable with the specified degree of variation that is relatively high from among variables that are included in the plurality of variables and have not yet been selected. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper. Selecting based on being relatively high in degree of variation could also recite mathematical relationship, which is mathematical concept.)
Claim 8 does not recite additional elements.
Regarding claim 9, the rejection of claim 5 is incorporated herein. Further, claim 9 recites the following abstract ideas:
specifying an amount in which the each variable appears in the plurality of mathematical models, (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
wherein the processing of selecting includes selecting a variable with the specified amount that is relatively large from among variables that are included in the plurality of variables and have not yet been selected. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper. Selecting based on being relatively large in amount could also recite mathematical relationship, which is mathematical concept.)
Claim 9 does not recite additional elements.
Regarding claim 10, the rejection of claim 5 is incorporated herein. Further, claim 10 recites the following abstract ideas:
wherein the processing of selecting includes randomly selecting a variable that is included in the plurality of variables and has not yet been selected. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
Claim 10 does not recite additional elements.
Regarding claim 11, the rejection of claim 5 is incorporated herein. Further, claim 11 recites the following additional element:
wherein the processing of selecting is executed until designation of any mathematical model of the plurality of mathematical models is accepted. (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 12, the rejection of claim 1 is incorporated herein. Further, claim 12 recites the following abstract ideas:
wherein the processing of determining includes determining the evaluation for the each mathematical model based on the importance level for the any variable set according to the acquired evaluation, the specified frequency, and a coefficient according to a term that appears in the each mathematical model. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper.)
Claim 12 does not recite additional elements.
Regarding claim 13, the rejection of claim 1 is incorporated herein. Further, claim 13 recites the following abstract ideas:
wherein the mathematical model includes a term formed by combining two or more variables among the plurality of variables. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper. Combining two or more variables to form a term could also recite mathematical relationship, which is mathematical concept.)
Claim 13 does not recite additional elements.
Regarding claim 14, the rejection of claim 1 is incorporated herein. Further, claim 14 recites the following abstract ideas:
with reference to information that enables conversion of the answer into the importance level for the any variable. (This is practical to perform in the human mind under its broadest reasonable interpretation aside from the recitation of generic computer components or by a human using a pen and paper. Conversion of answer into importance level for variable could also be done with mathematical calculation, which is mathematical concept.)
Claim 14 further recites following additional elements:
wherein the processing of acquiring includes acquiring an answer from a user that indicates the evaluation for any variable, and setting the importance level for the any variable based on the acquired answer (This is mere data gathering and outputting, an insignificant extra solution activity, which does not integrate the judicial exception into a practical application. The broadest reasonable interpretation of this claim is storing information in memory, which is a well-understood, routine conventional activity. See MPEP § 2106.05(d)(II)(iv). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 15, the following claim elements are additional elements:
An information processing method implemented by a computer, the method comprising (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
Regarding claim 16, the following claim elements are additional elements:
An information processing apparatus comprising: a memory; and a processor coupled to the memory, the processor being configured to perform processing including: (This falls under mere instructions to apply an exception. See MPEP § 2106.05(f). Therefore, this does not amount to significantly more than the judicial exception.)
The rest of claims 15 – 16 recite substantially similar subject matter to claim 1 respectively and are rejected with the same rationale, mutatis mutandis.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1 – 2, 4 – 9, 11 – 16 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al. (U.S. Pub. 2009/0089023 A1) in view of Feit et al. (U.S. Pub. 2014/0278738 A1).
Regarding Claim 1, Watanabe teaches
A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing comprising: acquiring evaluation for any variable that appears in at least any mathematical model of a plurality of mathematical models among a plurality of variables ([0061] of Watanabe states ”Thus, the inner-model factor value extracting part calculates the value of the factor obtained based on the transition of a factor in respective models and evaluation data, and the inter-model factor value extracting part calculates the value of the factor obtained based on the information on the factor in a plurality of models. Consequently, the inner-model factor value extracting part and the inter-model factor value extracting part determine the value of the factor from various points of view. Therefore, data indicating a more general value of the factor is obtained.” [0064] of Watanabe states “In an embodiment of the present invention, it is preferred that the model managing part further acquires application period data indicating an application period of the model and significance data indicating significance of each factor corresponding to each explanatory variable included in the regression equation of the model and accumulates the application period data and the significance data in the model recording part, and the inner-model factor value extracting part detects a change in the application period of the model based on the application period data accumulated in the model recording part regarding at least one phenomenon, extracts a factor that contributes to enhancement of a fitting degree of the model when the application period changes based on a change in the fitting degree of the model and a change in the significance data on each factor in the model before and after the change in the application period, and generates factor value data indicating a degree to which the extracted factor contributes to the model.”)
specifying a frequency of appearance of each variable of the plurality of variables in each mathematical model of the plurality of mathematical models ([0208] of Watanabe states ”The inter-model factor value extracting part 72 extracts a factor which has commoness and whose t-test value is stable at a predetermined level or more among the group of models M11 to M15 (Op622 in FIG. 18). Herein, whether or not the commoness of a factor is high is determined, for example, based on a ratio at which models including the factor occupy in the extracted group of models M11 to M15.” [0350] of Watanabe states “The factor procuring part 13 receives the designation of day factors from the model managing part 5 a, and searches the explanatory variable B for more appropriate modified day species and outputs them as day species (hereinafter, referred to as designated day species) indicated by the designated day factors. For example, the factor procuring part 13 measures the existence criterion on a time coordinate of the designated day species, and searches the explanatory variable DB 62 for more appropriate day species as the designated day species based on the existence criterion. The existence criterion on the time coordinate includes, for example, a relative distance indicating the overlapping degree and adjacency between the period indicated by a day species and the period indicated by the designated day species, an appearance frequency in a predetermined period of the period indicated by the designated day species, and the like.” [0351] of Watanabe states “The factor procuring part 13 can calculate the above relative distance, for example, by comparing an element value of the designated day species with an element value of each day species recorded on the element value table of the explanatory variable DB 62. Furthermore, the factor procuring part 13 can calculate the above appearance frequency by calculating the scattering degree of a value “1” in the element values of the designated day species.”)
and determining evaluation for the each mathematical model based on … and the specified frequency. ([0142] of Watanabe states “At a time of the instruction, the model managing part 5 receives regression equations before and after the change or application periods before and after the change, and evaluation data (a fitting degree, the significance of each factor, etc.) before and after the change, and passes them to the inner-model factor value extracting part 71. The inner-model factor value extracting part 71 calculates a fitting degree enhancement property by a change in factors or a fitting degree enhancement property by a change in an application period of the factor included in the model after the change, and records it in the factor value DB 65. The detail of the inner-model factor value extracting part 71 will be described later in detail.” [0179] of Watanabe states ”In the model after the change represented by M2, the factor “rainy season” corresponding to the explanatory variable X2 of the model before the change is changed to “late June to mid-July”. Due to the change in the factor, the numerical values of the respective weights β0 to β3 in M2 also become different from those in M1. Then, the fitting degree of analysis-estimation is enhanced from “0.64” to “0.75”. Thus, it can be determined that the fitting degree of analysis-estimation has been enhanced due to the change in the factor of the model.”[0208] of Watanabe states ”The inter-model factor value extracting part 72 extracts a factor which has commoness and whose t-test value is stable at a predetermined level or more among the group of models M11 to M15 (Op622 in FIG. 18). Herein, whether or not the commoness of a factor is high is determined, for example, based on a ratio at which models including the factor occupy in the extracted group of models M11 to M15.”)
However, Watanabe does not explicitly teach an importance level for the any variable set according to the acquired evaluation
Feit teaches that
an importance level for the any variable set according to the acquired evaluation ([0063] of Feit states “Alternatively, a user can be asked to assign a non-exclusive value to each factor. In the alternative example, the user can assign a value between one and seven to each value, with seven indicating a most important factor, and permitting the same numerical importance value to be given to multiple factors. Thereafter, to facilitate appropriate weighting, the sum of all numerical importance values can be determined. Each relative weight can be divided by the sum to resolve a weighting factor. Finally, each qualitative score can be multiplied by its relative weighting factor determined by its importance.”)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Watanabe with Feit. Watanabe teaches evaluating mathematical models based on factors/variables appearing in the models, including factor commonness across a plurality of models. Feit teaches acquiring user provided evaluations of factors and converting those evaluations into weighting factors/importance levels. One with the ordinary skill in the art would have been motivated to incorporate the teachings of Feit into the Watanabe to allow Watanabe’s model evaluation to further reflect user indicated importance of variables/factors for improvement on robustness and accuracy of the model evaluation and selection process. It would have been predictable combination of evaluation system to obtain user evaluations and converting them into importance weights.
Regarding claim 2, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Watanabe and Feit teaches
wherein the determining includes determining the evaluation for the each mathematical model based on the importance level for the any variable set according to the acquired evaluation, an importance level preset for each variable of remaining variables other than the any variable among the plurality of variables, and the specified frequency. ([0142] of Watanabe states “At a time of the instruction, the model managing part 5 receives regression equations before and after the change or application periods before and after the change, and evaluation data (a fitting degree, the significance of each factor, etc.) before and after the change, and passes them to the inner-model factor value extracting part 71. The inner-model factor value extracting part 71 calculates a fitting degree enhancement property by a change in factors or a fitting degree enhancement property by a change in an application period of the factor included in the model after the change, and records it in the factor value DB 65. The detail of the inner-model factor value extracting part 71 will be described later in detail.” [0179] of Watanabe states ”In the model after the change represented by M2, the factor “rainy season” corresponding to the explanatory variable X2 of the model before the change is changed to “late June to mid-July”. Due to the change in the factor, the numerical values of the respective weights β0 to β3 in M2 also become different from those in M1. Then, the fitting degree of analysis-estimation is enhanced from “0.64” to “0.75”. Thus, it can be determined that the fitting degree of analysis-estimation has been enhanced due to the change in the factor of the model.” [0208] of Watanabe states ”The inter-model factor value extracting part 72 extracts a factor which has commoness and whose t-test value is stable at a predetermined level or more among the group of models M11 to M15 (Op622 in FIG. 18). Herein, whether or not the commoness of a factor is high is determined, for example, based on a ratio at which models including the factor occupy in the extracted group of models M11 to M15.” [0063] of Feit states “Alternatively, a user can be asked to assign a non-exclusive value to each factor. In the alternative example, the user can assign a value between one and seven to each value, with seven indicating a most important factor, and permitting the same numerical importance value to be given to multiple factors. Thereafter, to facilitate appropriate weighting, the sum of all numerical importance values can be determined. Each relative weight can be divided by the sum to resolve a weighting factor. Finally, each qualitative score can be multiplied by its relative weighting factor determined by its importance.” [0064] of Feit states “A second (non-weighted) subset can be received or recorded in a value directly applicable to a partial score (e.g., portion of the qualitative score, score used in calculation of composite score(s) discussed infra). For the second subset, one or more qualitative inquiries can be scored (continuing with the earlier example, from one to seven), and no subsequent calculation occurs—the score is recorded and/or utilized “as-is.”” Feit states that the remaining items will be non-weighted)
Regarding claim 4, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Watanabe and Feit teaches
wherein information regarding the evaluation determined for the each mathematical model is output. ([0099] of Watanabe states ”The model information acquiring part 3 requests model information with respect to the respective information processing apparatuses 15 a to 15c via the IF part 2, and receive model information therefrom. The received model information is passed to the model managing part 5. The model information contains, for example, data indicating a regression equation of a model, data indicating a target phenomenon of the model and factors, evaluation data on the model, data indicating an analysis application period and a prediction application period of the model, and the like. The evaluation data on the model contains, for example, data indicating the fitting degree of a model (at least one of a fitting degree of analysis-estimation and a fitting degree of prediction result), and the significance of each factor of the model.” [0100] of Watanabe states “The model information acquiring part 3 may, for example, request and receive model information periodically. Alternatively, in each of the information processing apparatuses 15 a to 15 c, when a model is created or updated, model information may be sent to the model information acquiring part 3 automatically together with an update notification or a new creation notification.”)
Regarding claim 5, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Watanabe and Feit teaches
selecting a variable that has not yet been selected from among the plurality of variables, ([0318] of Watanabe states ”A screen G9 shown in FIG. 34 is an exemplary screen displaying the factors extracted by the model proposing part 11 in a selectable list. A list L3 on the screen G9 displays names of the extracted factors so that they can be selected. The user selects at least one factor desired to be added to the model from the list L3. The model proposing part 11 is notified of the factor selected by the user. The model proposing part 11 generates a regression equation of the model including the selected factor and sends it to the information processing apparatus 15 a” [0112] of Feit states “At 516, a determination is made regarding whether additional subjective evaluations are to be performed. If additional attributes or categories can be evaluated by a subject, methodology 500 returns to 514, where evaluations can be completed. If no additional evaluations remain to be completed, methodology 500 can proceed to 516. It is to be appreciated that subjective evaluation can occur earlier or elsewhere within methodology 500.” Feit teaches an iterative selection loop over unevaluated categories. It would be obvious for POSITA to apply the same loop to Watanabe.)
wherein the acquiring includes acquiring, every time the variable that has not yet been selected is selected from among the plurality of variables, evaluation from a user for the variable selected this time ([0084] of Feit states ”In addition to causing presentation of inquiries relating to subjective feedback with respect to a performance test, inquiry handling component 220 can cause presentation (as well as response and handling of response information) of one or more importance inquiries related to the subjective feedback. In a non-limiting example, a subject can be asked to rate the categories in which they provided subjective feedback in terms of their importance. For example, after a subjective inquiry, a subject can be solicited to rate, on a scale of one to seven, a particular category's importance in relation to other categories. In this example, the importance inquiry can be constructed rigidly or flexibly. A rigid inquiry can require a least to most important ranking of all categories with no ties. A flexible inquiry can permit non-exclusive ratings and allows a user to equally rank categories with regard to importance.” Watanabe provides the model set and supports re-evaluating models when a new factor is chosen and Feit provides the importance level factor for determination factor.)
, and the determining includes determining, every time the evaluation from the user is acquired, the evaluation for the each mathematical model based on an importance level set according to the evaluation for the variable for which the evaluation from the user has been acquired among the plurality of variables, and the specified frequency. ([0142] of Watanabe states “At a time of the instruction, the model managing part 5 receives regression equations before and after the change or application periods before and after the change, and evaluation data (a fitting degree, the significance of each factor, etc.) before and after the change, and passes them to the inner-model factor value extracting part 71. The inner-model factor value extracting part 71 calculates a fitting degree enhancement property by a change in factors or a fitting degree enhancement property by a change in an application period of the factor included in the model after the change, and records it in the factor value DB 65. The detail of the inner-model factor value extracting part 71 will be described later in detail.” [0179] of Watanabe states ”In the model after the change represented by M2, the factor “rainy season” corresponding to the explanatory variable X2 of the model before the change is changed to “late June to mid-July”. Due to the change in the factor, the numerical values of the respective weights β0 to β3 in M2 also become different from those in M1. Then, the fitting degree of analysis-estimation is enhanced from “0.64” to “0.75”. Thus, it can be determined that the fitting degree of analysis-estimation has been enhanced due to the change in the factor of the model.” [0208] of Watanabe states ”The inter-model factor value extracting part 72 extracts a factor which has commoness and whose t-test value is stable at a predetermined level or more among the group of models M11 to M15 (Op622 in FIG. 18). Herein, whether or not the commoness of a factor is high is determined, for example, based on a ratio at which models including the factor occupy in the extracted group of models M11 to M15.” [0063] of Feit states “Alternatively, a user can be asked to assign a non-exclusive value to each factor. In the alternative example, the user can assign a value between one and seven to each value, with seven indicating a most important factor, and permitting the same numerical importance value to be given to multiple factors. Thereafter, to facilitate appropriate weighting, the sum of all numerical importance values can be determined. Each relative weight can be divided by the sum to resolve a weighting factor. Finally, each qualitative score can be multiplied by its relative weighting factor determined by its importance.” [0064] of Feit states “A second (non-weighted) subset can be received or recorded in a value directly applicable to a partial score (e.g., portion of the qualitative score, score used in calculation of composite score(s) discussed infra). For the second subset, one or more qualitative inquiries can be scored (continuing with the earlier example, from one to seven), and no subsequent calculation occurs—the score is recorded and/or utilized “as-is.”” Feit states that the remaining items will be non-weighted)
Regarding claim 6, the rejection of claim 5 is incorporated herein. Furthermore, the combination of Watanabe and Feit teaches
wherein the selecting is executed in a case where a variable that has not yet been selected remains among the plurality of variables. ([0112] of Feit states “At 516, a determination is made regarding whether additional subjective evaluations are to be performed. If additional attributes or categories can be evaluated by a subject, methodology 500 returns to 514, where evaluations can be completed. If no additional evaluations remain to be completed, methodology 500 can proceed to 516. It is to be appreciated that subjective evaluation can occur earlier or elsewhere within “methodology 500.)
Regarding claim 7, the rejection of claim 5 is incorporated herein. Furthermore, the combination of Watanabe and Feit teaches
wherein the selecting is executed in response to an instruction from the user. ([0118] of Watanabe states “The request for supporting model creation received by the condition acquiring part 4 may be a request for creating a replacement model or a replacement factor obtained by changing an existing model created by an information processing apparatus of a request origin so as to enhance a fitting degree, or a request for creating a new model. In the case of the former, the model condition data contains, for example, information indicating the existing model created by the information processing apparatus of a request origin, information indicating factors desired to be retained or factors desired to be changed among factors of the existing model. The condition acquiring part 4 can acquire such model condition data simultaneously with a model creation request, or can acquire such model condition data by requesting the model condition data with respect to the information processing apparatus of a request origin after receiving a model creation request.” [0148] of Watanabe states “The model condition data contains, for example, data indicating a regression equation of an existing model requested to be replaced in the information processing apparatus 15 a and a replacement target range in the existing model. More specifically, the data indicating the replacement target range contains data indicating whether the existing model is replaced on a model basis or on a factor basis. Furthermore, data specifying a factor to be replaced is also contained in the data indicating the replacement target range.” Watanabe explains the factor chosen for further processing is selected based on what the requester instructs should be modified.)
Regarding claim 8, the rejection of claim 5 is incorporated herein. Furthermore, the combination of Watanabe and Feit teaches
specifying a degree of variation in the evaluation for the each mathematical model in a case where the evaluation from the user for the each variable satisfies a predetermined condition, ([0084] of Feit states ”In addition to causing presentation of inquiries relating to subjective feedback with respect to a performance test, inquiry handling component 220 can cause presentation (as well as response and handling of response information) of one or more importance inquiries related to the subjective feedback. In a non-limiting example, a subject can be asked to rate the categories in which they provided subjective feedback in terms of their importance. For example, after a subjective inquiry, a subject can be solicited to rate, on a scale of one to seven, a particular category's importance in relation to other categories. In this example, the importance inquiry can be constructed rigidly or flexibly. A rigid inquiry can require a least to most important ranking of all categories with no ties. A flexible inquiry can permit non-exclusive ratings and allows a user to equally rank categories with regard to importance.” [0179] of Watanabe states “In the model after the change represented by M2, the factor “rainy season” corresponding to the explanatory variable X2 of the model before the change is changed to “late June to mid-July”. Due to the change in the factor, the numerical values of the respective weights β0 to β3 in M2 also become different from those in M1. Then, the fitting degree of analysis-estimation is enhanced from “0.64” to “0.75”. Thus, it can be determined that the fitting degree of analysis-estimation has been enhanced due to the change in the factor of the model.” Watanabe measures how much the model evaluation changes when a factor is changed which corresponds to specifying a degree of variation and evaluation from user can be obtained as Feit received user side evaluation input.)
wherein the processing of selecting includes selecting a variable with the specified degree of variation that is relatively high from among variables that are included in the plurality of variables and have not yet been selected. ([0175] of Watanabe states “First, the inner-model factor value extracting part 71 determines whether or not a fitting degree of analysis-estimation has changed largely before and after the change in the factor of the model (Op422)… Whether or not the value of a fitting degree of analysis-estimation has changed largely can be determined based on a predetermined threshold value, for example.” [0180] of Watanabe states “Then, the fitting degree of analysis-estimation is enhanced from “0.64” to “0.75”. Thus, it can be determined that the fitting degree of analysis-estimation has been enhanced due to the change in the factor of the model. Furthermore, the weight of the factor “late June to mid-July” increases to “+13.2” with respect to the t-test value “+1.8” of the weight of the factor “rainy season”. Compared with this change amount “11.4”, the change amount of the t-test values of the weights of the other factors “beginning of next week” and “Wednesday, Thursday, Friday in winter” is 0.1 to 0.2, which is ⅕ or less. In such a case, the inner-model factor value extracting part 71 can determine that the enhancement of a fitting degree of analysis-estimation by a change in a factor of a model has occurred due to the factor “late June to mid-July”.” Watanabe uses a predetermined threshold to decide whether the change in model evaluation is large enough to matter.)
Regarding claim 9, the rejection of claim 5 is incorporated herein. Furthermore, the combination of Watanabe and Feit teaches
specifying an amount in which the each variable appears in the plurality of mathematical models, ([0208] of Watanabe states ”The inter-model factor value extracting part 72 extracts a factor which has commoness and whose t-test value is stable at a predetermined level or more among the group of models M11 to M15 (Op622 in FIG. 18). Herein, whether or not the commoness of a factor is high is determined, for example, based on a ratio at which models including the factor occupy in the extracted group of models M11 to M15. Whether or not the t-test value of the factor is stable at a predetermined level or more is determined, for example, based on whether or not the t-test value of the factor is always equal to or more than a predetermined threshold value.” Watanabe measures commonness using the ratio of models including the factor.)
wherein the processing of selecting includes selecting a variable with the specified amount that is relatively large from among variables that are included in the plurality of variables and have not yet been selected. ([0200] of Watanabe states ”The inter-model factor value extracting part 72 can perform the determination based on the data in the model instance DB 63. For example, the inter-model factor value extracting part 72 refers to all the records having the same objective variable ID from the model instance DB 63, and can use a group of factors included in models indicated by the records and the significance of each factor for the above determination. Thus, a factor having high commoness and contributing to the enhancement of a fitting degree in a plurality of models having the same objective variable can be extracted.” Watanabe finds the factor with high commonness.)
Regarding claim 11, the rejection of claim 5 is incorporated herein. Furthermore, the combination of Watanabe and Feit teaches
wherein the processing of selecting is executed until designation of any mathematical model of the plurality of mathematical models is accepted. ([0297] of Watanabe states ”The step replacement is a search procedure for presenting replacement models or replacement factors one at a time so that the user can select, and the batch replacement is a search procedure for presenting all the replacement models or replacement factors at once. The selection result is used, for example, when the model proposing part 11 determines whether to present a group of similar factors or a group of similar models one at a time or to present them at once in Op14 or Op17 shown in FIG. 7.” [0161] of Watanabe states “The similar model space creating part 92 creates a plurality of replacement model candidates obtained by replacing a part of the factors in the reference model by similar factors, based on the similar factor space. Then, the model proposing part 11 requests the distance calculating part 8 to calculate the distances (similarities) between the reference model and a plurality of replacement model candidates. Thus, the similar model space creating part 92 obtains information indicating a group of replacement models close to the reference model and the distances thereof to generate similar model space data.” [0311] of Watanabe states “When the user clicks on the “OK” button, the model proposing part 11 creates a replacement model obtained by replacing the factor to be replaced X1 “Wednesday, Thursday, Friday in winter” by “Thursday and Friday at the end of the year”. When a “NO” button is clicked on, the model proposing part 11 displays another similar factor similar to the factor X3 “Wednesday, Thursday, Friday in winter”.” Watanabe teaches an iterative selection process in which multiple mathematical model candidates are presented to the user and the selection continues until one of the presented model is accepted/designated by the user.)
Regarding claim 12, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Watanabe and Feit teaches
wherein the processing of determining includes determining the evaluation for the each mathematical model based on the importance level for the any variable set according to the acquired evaluation, the specified frequency, and a coefficient according to a term that appears in the each mathematical model. ([0142] of Watanabe states “At a time of the instruction, the model managing part 5 receives regression equations before and after the change or application periods before and after the change, and evaluation data (a fitting degree, the significance of each factor, etc.) before and after the change, and passes them to the inner-model factor value extracting part 71. The inner-model factor value extracting part 71 calculates a fitting degree enhancement property by a change in factors or a fitting degree enhancement property by a change in an application period of the factor included in the model after the change, and records it in the factor value DB 65. The detail of the inner-model factor value extracting part 71 will be described later in detail.” [0179] of Watanabe states ”In the model after the change represented by M2, the factor “rainy season” corresponding to the explanatory variable X2 of the model before the change is changed to “late June to mid-July”. Due to the change in the factor, the numerical values of the respective weights β0 to β3 in M2 also become different from those in M1. Then, the fitting degree of analysis-estimation is enhanced from “0.64” to “0.75”. Thus, it can be determined that the fitting degree of analysis-estimation has been enhanced due to the change in the factor of the model.” [0208] of Watanabe states ”The inter-model factor value extracting part 72 extracts a factor which has commoness and whose t-test value is stable at a predetermined level or more among the group of models M11 to M15 (Op622 in FIG. 18). Herein, whether or not the commoness of a factor is high is determined, for example, based on a ratio at which models including the factor occupy in the extracted group of models M11 to M15.” [0063] of Feit states “Alternatively, a user can be asked to assign a non-exclusive value to each factor. In the alternative example, the user can assign a value between one and seven to each value, with seven indicating a most important factor, and permitting the same numerical importance value to be given to multiple factors. Thereafter, to facilitate appropriate weighting, the sum of all numerical importance values can be determined. Each relative weight can be divided by the sum to resolve a weighting factor. Finally, each qualitative score can be multiplied by its relative weighting factor determined by its importance.” [0005] of Watanabe states “The following Expression (1) is an example of a regression equation of linear multiple regression. In the following Expression (1), Y is an objective variable, X1 and X2 are explanatory variables, and a, b and c are constants. In particular, b and c are called partial regression coefficients.” [0178] of Watanabe states “In the model before the change represented by M1, β0 is a weight (parameter) of a constant term X0, and β1 to β3 are respective weights of explanatory variables X1 to X3.” [0322] of Watanabe states “Specifically, the explanatory variables X1 and X2 have correlations. The weight (coefficient) representing the correlation degree between X1 and X2 is a12. Similarly, the explanatory variables X2 and X3 and the explanatory variables X1 and X3 also have correlations with weights (coefficients) a23 and a13.”)
Regarding claim 13, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Watanabe and Feit teaches
wherein the mathematical model includes a term formed by combining two or more variables among the plurality of variables. ([0078] of Watanabe states “thereby extracting a factor having a predetermined relationship with the designated factor from the model recording part and recording factor data represented by a time factor value of the extracted factor or a complex time factor value obtained by an OR or an AND of the time factor value of the extracted factor and a time factor of the designated factor in the model recording part as factor data on a modified factor of the designated factor.” [0359] of Watanabe states “In the case where Yes(Y) is determined in Op703, the factor procuring part 13 creates day species data obtained by an OR and an AND of the extracted day species and the designated day species as a complex day factor and returns the day species data to the model managing part 5 a. Furthermore, the factor procuring part 13 can record the created complex day factor in the explanatory variable DB 62.” Watanabe teaches a model term formed by combining multiple factors into a complex factor that is recorded as an explanatory variable in the model.)
Regarding claim 14, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Watanabe and Feit teaches
wherein the processing of acquiring includes acquiring an answer from a user that indicates the evaluation for any variable, and setting the importance level for the any variable based on the acquired answer with reference to information that enables conversion of the answer into the importance level for the any variable. ([0062] of Feit states “In an example, a first subset of information is received to be weighted. Weighting of qualitative factors can be accomplished according to methods similar to the scaling above (e.g., by ranges, standard deviation, et cetera). In alternative or complementary embodiments, weighting can be accomplished according to subjective factors, such as relative importance as viewed by test subjects, observers, or administrators (e.g., test designers, system designers, test managers).” [0063] of Feit states “Alternatively, a user can be asked to assign a non-exclusive value to each factor. In the alternative example, the user can assign a value between one and seven to each value, with seven indicating a most important factor, and permitting the same numerical importance value to be given to multiple factors. Thereafter, to facilitate appropriate weighting, the sum of all numerical importance values can be determined. Each relative weight can be divided by the sum to resolve a weighting factor. Finally, each qualitative score can be multiplied by its relative weighting factor determined by its importance.” [0084] of Feit states “In a non-limiting example, a subject can be asked to rate the categories in which they provided subjective feedback in terms of their importance. For example, after a subjective inquiry, a subject can be solicited to rate, on a scale of one to seven, a particular category's importance in relation to other categories.” Feit teaches setting the importance level for the variable based on the acquired answer with reference to information that enables conversion of the answer into the importance level. )
Claims 15, 16 recite substantially similar subject matter as claim 1 respectively, and are rejected with the same rationale, mutatis mutandis.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al. (U.S. Pub. 2009/0089023 A1) in view of Feit et al. (U.S. Pub. 2014/0278738 A1), further in view of Chai et al. (foreign Pub. CN109800048A ).
Regarding claim 3, the rejection of claim 1 is incorporated herein. Furthermore, the combination of Watanabe and Feit does not explicitly teaches
outputting information regarding the each mathematical model in a mode that corresponds to the evaluation determined for the mathematical model.
However, Chai teaches that
outputting information regarding the each mathematical model in a mode that corresponds to the evaluation determined for the mathematical model. (Chai states “Further, the types of models includes two disaggregated models and regression model. Further, the model index include serial number corresponding with each machine learning model, model name, algorithm, Significant variable number, Receiver operating curve, Kolmogorov-Smirnove test, promotes song at automatic configuration Line, progress, creation time and operation…Beneficial effects of the present invention: the present invention is by the result of the different machines learning model trained by the same data set It compares, and shows the difference between different machines learning model in a manner of visual.The present invention can support multiple moulds Comparison between type result, by the parameter information of each model, significant variable information and model error assessment result information with The mode of chart is shown, and visual pattern is compared each model, selects optimal mould so as to user's quicklook Type.” Chai teaches outputting in an evaluation mode and mode corresponds to selected evaluation type.)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Chai with the combination of Watanabe and Feit. Watanabe teaches evaluating mathematical models based on factors/variables appearing in the models, including factor commonness across a plurality of models. Feit teaches acquiring user provided evaluations of factors and converting those evaluations into weighting factors/importance levels. Chai teaches displaying multiple models, corresponding model indicies, and comparative result figures to facilitate user comparison and model selection. One with the ordinary skill in the art would have been motivated to incorporate the teachings of Chai into the combination of Watanabe and Feit to output the evaluated models in a comparative manner that would allow a user to more easily review candidate models and select an appropriate set. It would have been predictably improved the presentation and usability of the evaluation results while keeping the combined evaluation system of Watanabe and Feit.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Watanabe et al. (U.S. Pub. 2009/0089023 A1) in view of Feit et al. (U.S. Pub. 2014/0278738 A1), further in view of Attenberg et al. (U.S. Pub. 2011/0173037 A1).
Regarding claim 10, the rejection of claim 5 is incorporated herein. Furthermore, the combination of Watanabe and Feit teaches
wherein the processing of selecting includes [randomizing order of] a variable that is included in the plurality of variables and has not yet been selected. ([0112] of Feit states “At 516, a determination is made regarding whether additional subjective evaluations are to be performed. If additional attributes or categories can be evaluated by a subject, methodology 500 returns to 514, where evaluations can be completed. If no additional evaluations remain to be completed, methodology 500 can proceed to 516. It is to be appreciated that subjective evaluation can occur earlier or elsewhere within methodology 500.” [0095] of Feit states “For example, one or more tasks, and associated performance and inquiry evaluations, can be standardized. The standardized tasks and evaluations can be randomized in order of execution, and evaluations can be modified (e.g., “flip” positives to negatives, counter-balancing) between subjects to avoid skewing results across all tasks and questions.” Under BRI, randomly selecting a variable that has not yet been selected is reasonably met by randomizing the order in which remaining evaluation items are processed. Feit is randomizing the order of the remaining evaluation items in the same type of user evaluation loop.)
However, the combination of Watanabe and Feit does not explicitly teach randomly selecting
Attenberge teaches that
randomly selecting a [parameters] ([0015] of Attenberg states ”These different sources of information can be combined in arbitrary mixes in accordance with any budget, class balance parameters, or any sampling ratio of any sub-concepts of interest. Additionally or alternatively, hybrid learning techniques can be utilized that combine guided learning approaches with active learning approaches and random sampling, thereby leveraging labelers for additional information. Accordingly, a guided learning approach and a hybrid learning approach can be provided based on the budget and skew parameters.” Attenberg shows random sampling is a known alternative selection.)
It would have been obvious to one with ordinary skill in the art before the effective filing date of the invention to combine the teachings of Attenberg with the combination of Watanabe and Feit. Watanabe teaches evaluating mathematical models based on factors/variables appearing in the models, including factor commonness across a plurality of models. Feit teaches acquiring user provided evaluations of factors and converting those evaluations into weighting factors/importance levels. Attenberg teaches guided learning, active learning, and random sampling techniques for iteratively acquiring additional human provided information. One with the ordinary skill in the art would have been motivated to incorporate the teachings of Attenberg into the combination of Watanabe and Feit to improve how additional user evaluation information is iteratively acquired, selected, or prioritized during the evaluation process. It would have been predictably combination to improve the efficiency and robustness of obtaining user input while keeping the combined evaluation system of Watanabe and Feit.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BYUNGKWON HAN whose telephone number is (571)272-5294. The examiner can normally be reached M-F: 9:00AM-6PM PST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached at (571)272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BYUNGKWON HAN/ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121