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 .
Claim Objections
Claim 2 is objected to because of the following informalities: the claim contains limitation:
“the second prediction algorithm model comprises a CTR model realized by”, must have "full, clear, concise, and exact terms" and the spec does not appear to provide what the acronym stands for.
“by gradient boost decision tree algorithm + logistic regression algorithm or gradient boost decision tree algorithm + factorization machine algorithm” the claim use a symbol “+” in place of proper claim terminology. The use of the symbols such as “+” renders the scope and meaning of the claim unclear. Appropriate wording such as “and” or “or” should be used instead to clearly set forth the claim subject matter.
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.
Claim(s) 1 and 7 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 pre-AIA the applicant regards as the invention.
Regarding claim 1 and analogous claim 7 recite the limitation “the first number” and “the second number”. There is insufficient antecedent basis for the limitation in the claim, which arises from the ambiguity of reference. Specifically, the terms lack clarity because it is not clearly defined or linked to a prior element in the claim. For a more precise understanding, the claim should explicitly define or identify terms before refereeing it. For examination purposes, the examiner will interpret “the first number” as “a first number of knowledge items” and “the second number” as “a second number of knowledge items”
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
a data obtaining module, to obtain current searching…, a first prediction module to use a first prediction algorithm model…, a second prediction module to use a second prediction algorithm model…, a knowledge recommendation module, to provide…; in claim 7.,
a prediction information confirmation module, to provide…, ; in claim(s) 8 and 9,
a knowledge resource confirmation module, to receive second feedback…, in claim(s) 10 and 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim(s) 7 – 11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AlA), 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 pre-AlA the applicant regards as the invention.
Claim limitation:
a data obtaining module, to obtain current searching…, a first prediction module to use a first prediction algorithm model…, a second prediction module to use a second prediction algorithm model…, a knowledge recommendation module, to provide…; in claim 7.,
a prediction information confirmation module, to provide…, ; in claim(s) 8 and 9,
a knowledge resource confirmation module, to receive second feedback…, in claim(s) 10 and 11.
invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function.
The correct requirement for satisfying the definiteness requirement is that the corresponding structure (or material or acts) of a means- (or step-) plus-function limitation must be disclosed in the specification itself in a way that one skilled in the art will understand what structure (or material or acts) will perform the recited function. If there is no disclosure of structure, material or acts for performing the recited function, the claim fails to satisfy the requirements of 35 U.S.C. 112(b). See Atmel Corp. v. Information Storage Devices, Inc., 198 F.3d 1374, 1381, 53 USPQ2d 1225, 1230 (Fed. Cir. 1999).
Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1 – 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more.
In step 1, of the 101-analysis set forth in the MPEP 2106, the examiner has determined
that the following limitations recite a process that, under the broadest reasonable interpretation, falls within one or more statutory categories (processes).
In step 2A prong 1, of the 101-analysis set forth in MPEP 2106, the examiner has determined
that the following limitations recite a process that, under broadest reasonable interpretation, covers
a mental process but for the recitation of generic computer components:
Regarding claim 1:
recommending at least one knowledge resource output by the knowledge recommendation model to the user;
(i.e.: the broadest reasonable interpretation, the claim recites abstract idea: mental process: It involves evaluating available knowledge resources and selecting one or more resources to suggest to a user based on perceived relevance or suitability. See (MPEP 2106.04)).
If the claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process, but for the recitation of generic computer components, then it falls within the mental process. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 of the 101-analysis, set forth in MPEP 2106, the examiner has determined that
the following additional elements do not integrate this judicial exception into a practical application:
obtaining current searching information of a user for certain knowledge in factory production, job characteristic information of the user, and historical feedback information of the user for at least one knowledge item and/or knowledge resource in the past;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g)).
using a first prediction algorithm model for learning based on historical searching information, job feature information, and historical feedback information of the user and other users to analyze obtained information, to obtain first prediction information including a first number of knowledge items;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
using a second prediction algorithm model to perform fusion sorting on the first prediction information and thereby obtain second prediction information including a second number of knowledge items;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation which does not amount to more than a recitation of the words "apply it" (or an equivalent), such as mere instructions to implement an abstract idea on a computer. See MPEP 2106.05(f)).
wherein the first number is greater than the second number, and the second number is greater than or equal to 1;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h)).
providing the second number of knowledge items to a knowledge recommendation model;
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the claim recites limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity, See MPEP (2106.05(g)).
wherein the knowledge recommendation model is obtained by training with training samples formed by establishing corresponding relationship between various knowledge resources involved in factory production and corresponding knowledge items, wherein the knowledge items are taken as input samples and corresponding knowledge resources are taken as output samples.
(i.e.: deemed insufficient to transform the judicial exception to a patentable invention because the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h)).
In Step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the
claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception:
Regarding limitation (II and III), recite mere application of the abstract idea or mere instructions to implement an abstract idea on a computer are deemed insufficient to transform the judicial exception to a patentable invention because the limitations generally apply the use of a generic computer and/or process with the judicial exception, see MPEP 2106.05(f).
Regarding limitation (I and V), additional elements considered extra/post solution activity, as analyzed above, are activity that are well-understood routine and conventional, specifically: the courts have recognized the computer functions as well‐understood, routine, and conventional functions.
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
Regarding limitation (IV and VI), additional elements are deemed insufficient to transform the judicial exception to a patentable invention to a patentable invention because they generally link the judicial exception to the technology environment, see MPEP 2106.05(h).
Claim 7, recite similar subject matter as claim 1, so is rejected under the same rationale.
Regarding claim 2, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
wherein: the first prediction algorithm model comprises an algorithm model realized by a logistic regression algorithm or a collaborative filtering algorithm; and the second prediction algorithm model comprises a CTR model realized by gradient boost decision tree algorithm + logistic regression algorithm or gradient boost decision tree algorithm + factorization machine algorithm.
The recitation in the additional limitation simply links the judicial exception to a field of use and/or technology environment, see MPEP 2106.05(h).
Limitations directed to field of use cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Regarding claim 3, dependent upon claim 2, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
before inputting the second number of knowledge items to a knowledge recommendation model, providing the second prediction information including the second number of knowledge items to the user for confirmation, and receiving first feedback information of the user for the second number of knowledge items;
(i.e.: the broadest reasonable interpretation, the claim recites an abstract idea, specifically a mental process. It involves presenting information for review, get user feedback and evaluate whether the information is acceptable. See (MPEP 2106.04)).
correcting the second prediction information according to the first feedback information to obtain a corrected second number of knowledge items.
(i.e.: the broadest reasonable interpretation, the claim recites an abstract idea, specifically a mental process. It involves reviewing feedback, evaluating how the information should be modified and adjust the information accordingly. See (MPEP 2106.04)).
Claim 8, recite similar subject matter as claim 3, so is rejected under the same rationale.
Regarding claim 4, dependent upon claim 3, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
providing the first feedback information or corrected second prediction information to the first prediction algorithm model for learning.
The recitation in the additional limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity and well-understood routine and conventional (2106.05(d)).
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
Claim 9, recite similar subject matter as claim 4, so is rejected under the same rationale.
Regarding claim 5, dependent upon claim 1, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
receiving second feedback information of the user for at least one knowledge resource recommended; and providing the second feedback information to the first prediction algorithm model for learning.
The recitation in the additional limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity and well-understood routine and conventional (2106.05(d)).
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
Claim 10, recite similar subject matter as claim 5, so is rejected under the same rationale.
Regarding claim 6, dependent upon claim 5, and fail to resolve the deficiencies identified above by
integrating the judicial exception into a practical application, or introducing significantly more than the judicial exception. The claim recites:
providing the second number of knowledge items and a knowledge resource with which the user interacts more as a training sample to the knowledge recommendation model for further training.
The recitation in the additional limitation directed to mere data gathering as deemed insufficient to transform the judicial exception because claimed elements are considered insignificant extra-solution activity and well-understood routine and conventional (2106.05(d)).
Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); TL| Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network). See MPEP 2106.05(d)(II).
The additional limitations as analyze failed to integrate a judicial exception into a practical application at Step 2A and provide an inventive concept in Step 2B, per the analysis above.
Claim 11, recite similar subject matter as claim 6, so is rejected under the same rationale.
Claim Rejections - 35 USC § 103
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, 5 – 6 and 10 – 11 are rejected under 35 U.S.C. 103 as being unpatentable over Lewis et al., Pub. No.: US20210350251A1 in view of Schmirler et al., Pub. No.: US20210065020A1 and Zhu et al., Pub. No.: US20210049478A1.
Regarding claim 1, Lewis teaches: A method for knowledge recommendation comprising:
obtaining current searching information of a user for certain knowledge in factory production, job characteristic information of the user, and historical feedback information of the user for at least one knowledge item and/or knowledge resource in the past;
(Lewis, “[0024] … Upon receiving (at block 400) a user description of a technical problem 112 from a user computer (not shown), the support manager 109 accesses (at block 402) the user profile database 200 to obtain user profile information 200, for the user initiating the request, including user information 204 [obtaining current searching information of a user for certain knowledge in factory production], systems specification 206 [job characteristic information of the user], historical records 208 [and historical feedback information of the user for at least one knowledge item and/or knowledge resource in the past] of previously applied solutions to the user computing system, regulatory requirements 210, etc...”)
using a first prediction algorithm model for learning based on historical searching information, job feature information, and historical feedback information of the user and other users to analyze obtained information, to obtain first prediction information including a first number of knowledge items;
(Lewis, “[0012]… a machine learning module (MLM) 124 [using a first prediction algorithm model for learning] to receive as input user profile information 200 [job feature information], from a user profile database 300, the user description of the technical problem 112, and parsed description 114 on the user technical support request [based on historical searching information] and generate ranks 126 of the result set of answer files 122, where the ranks 126 indicate a confidence or relevance score, such as between zero and one, indicating a strength or relevance of the answer files in the result set 122 to the user description 112; a filter program 128 to narrow the result set 122 based on criteria 130, such as usage information, historical records 208 [and historical feedback information of the user and other users to analyze obtained information] (FIG. 2) of solutions/fixes the user previously applied, and regulatory requirements 210 (FIG. 2) to produce a subset of answer files 134 [to obtain first prediction information including a first number of knowledge items] that satisfy the criteria 130; and a machine learning module (MLM) trainer 136 to receive historical solution information 300 I, including previous input 304 to the machine learning module 124 and user feedback 140, to train the machine learning module 124 to produced adjusted ranks of answer files 138 based on user feedback to improve the predictability of the relevance of answer files for a particular user. (Watson is a trademark of International Business Machines Corporation throughout the world).”)
using a second prediction algorithm model to perform fusion sorting on the first prediction information and thereby obtain second prediction information including a second number of knowledge items;
(Lewis, “[0028] …The filter program 128 determines (at block 426) answer files 134 describing features supported in the system subject to the solution/fix presented in the answer files 134 [using a second prediction algorithm model to perform fusion sorting on the first prediction information and] that are not supported in the user system. The filter program 128 excludes (at block 428) answer files from the modified subset 134 describing system features not supported in the user system 206. The resulting modified subset of answer files 134 narrowed by the filter program 128 may then be returned (at block 430) to the user to consider [thereby obtain second prediction information including a second number of knowledge items]. The support manager 109 may save (at block 432) in a historical solution record 300 k the user ID 302, input to machine learning module 304, output ranked answer files 126 from machine learning module 124 in field 306, and final modified subset of answer files 134 delivered to the user in field 308.”)
wherein the first number is greater than the second number, and the second number is greater than or equal to 1;
(Lewis, “[0028] …The filter program 128 excludes (at block 428) answer files from the modified subset 134 describing system features not supported in the user system 206. The resulting modified subset of answer files 134 narrowed by the filter program 128 may then be returned (at block 430) to the user to consider [wherein the first number is greater than the second number, and the second number is greater than or equal to 1] (i.e.: the narrowed subset is smaller than the original result set). The support manager 109 may save (at block 432) in a historical solution record 300 k the user ID 302, input to machine learning module 304, output ranked answer files 126 from machine learning module 124 in field 306, and final modified subset of answer files 134 delivered to the user in field 308.”)
wherein the knowledge items are taken as input samples and corresponding knowledge resources are taken as output samples.
(Lewis, “[0023] FIG. 3 illustrates an embodiment of a historical solution record 300 i in the historical solutions database 300 including information on past received answer files, including: a user identifier 302 for which the solution was provided; all the input 304 [wherein the knowledge items are taken as input samples] to the machine learning module 124 that produced the outputted ranks for answer files 306 for a technical support request [and corresponding knowledge resources are taken as output samples]; final modified subset of answer files provided to the user 308, such as files 134;…”)
Lewis does not teach:
providing the second number of knowledge items to a knowledge recommendation model; and recommending at least one knowledge resource output by the knowledge recommendation model to the user;
involved in factory production and corresponding knowledge items,
wherein the knowledge recommendation model is obtained by training with training samples formed by establishing corresponding relationship between various knowledge resources
Schmirler teaches:
providing the second number of knowledge items to a knowledge recommendation model; and recommending at least one knowledge resource output by the knowledge recommendation model to the user;
(Schmirler, “[0023] … In some embodiments, the training content is generated based on the captured (e.g., recorded audio and image or other visual data) interaction between the field user and the remote expert while performing a particular task via the training system. For example, the training content may include recorded steps based on instructions (e.g., verbal guidelines) provided by the remote expert and a response provided by the field user indicating a completed step, as performed via the training system. Such training content [providing the second number of knowledge items to a knowledge recommendation model] may be stored and may be retrieved from a database or other suitable storage component at a later time, so as to instruct a future field user to perform a similar or the same task [and recommending at least one knowledge resource output by the knowledge recommendation model to the user]. Additionally, the training system may automatically detect a variant or unexpected response or feedback provided by the future field user indicating a particular step could not be completed, such as due to an unclear instruction…”)
involved in factory production and corresponding knowledge items,
(Schmirler, “[0026] For purposes of discussion, the description of the training system provided herein is made with reference to performing a task in an industrial environment [involved in factory production and corresponding knowledge items]. However, it should be noted that the training system, as described herein, is not limited to such environments. The training system may be used in various other fields and applications. For example, the training system may be applied to performing tasks in an athletic environment, an academic environment, a natural environment, and so forth.”)
Schmirler and Lewis are related to the same field of endeavor (i.e.: training neural network to provide technical solutions). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Schmirler with teachings of Lewis to add multimedia based assistance tailored to a requested operation to enhance the delivery and adaptation of assistance content. (Schmirler, Abstract).
Lewis in view of Schmirler do not teach:
wherein the knowledge recommendation model is obtained by training with training samples formed by establishing corresponding relationship between various knowledge resources
Zhu teaches:
wherein the knowledge recommendation model is obtained by training with training samples formed by establishing corresponding relationship between various knowledge resources
(Zhu, “[0067] In some embodiments of the present specification, pre-training the classification model includes the following steps: obtaining a training sample data set, where the training sample data [wherein the knowledge recommendation model is obtained by training with training samples] set includes a feature relationship sample between a parameter instance sample [formed by establishing corresponding relationship between various knowledge resources] and an operator entity sample and a sample label corresponding to the feature relationship sample, and the sample label includes a white sample label and a black sample label; and training the classification model based on a tree-type model, where the classification model associates the feature relationship sample with the sample label.”)
Zhu, Lewis and Schmirler are related to the same field of endeavor (i.e.: training neural network to provide technical solutions). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Zhu with teachings of Lewis and Schmirler to add the use of a knowledge graph to model relationships among entities associated with technical problems, solutions or users to enhance inference based generation of additional relationships or recommendations. (Zhu, Abstract).
Claim 7, recites limitations analogous to claim 1, so is rejected under the same rationale.
Regarding claim 5, Lewis in view of Schmirler and Zhu teach the method of claim 1.
Lewis further teaches: receiving second feedback information of the user for at least one knowledge resource recommended; and providing the second feedback information to the first prediction algorithm model for learning.
(Lewis, “[0032] With the embodiment of FIG. 5, the MLM trainer 136 may generate adjusted ranks 138 for the answer files from the machine learning module 124 ranks 126 based on user feedback 140 [receiving second feedback information of the user for at least one knowledge resource recommended] and then retrain the machine learning module 124 to produce the adjusted ranks 126. In this way, user feedback is used to improve the ranking the machine learning module 124 will output for answer files based on input specific to the user profile and criteria. This training further optimizes the final output subset of answer files 134 [and providing the second feedback information to the first prediction algorithm model for learning] by improving the accuracy of the rankings to predict relevancy of solutions/fixes in answer files presented to the user.”)
Claim 10, recites limitations analogous to claim 5, so is rejected under the same rationale.
Regarding claim 6, Lewis in view of Schmirler and Zhu teach the method of claim 5.
Lewis further teaches: providing the second number of knowledge items and a knowledge resource with which the user interacts more as a training sample to the knowledge recommendation model for further training.
(Lewis, “[0030] FIG. 5 illustrates an embodiment of operations performed by the machine learning module (MLM) trainer 136 and the machine learning module 124 to retrain the machine learning module 124 to produce output [as a training sample to the knowledge recommendation model for further training] comprising user adjusted ranks of answer files 138 based user feedback 140. Upon receiving (at block 500) user feedback 140 on ranks in a modified subset 134 returned to the user, the MLM trainer 136 generates (at block 502) adjusted ranks for answer files 138 based on the user feedback 140. For instance, the user feedback 140 may provide specific rank values or may provide indication of accept or reject the output ranks 126 [providing the second number of knowledge items and a knowledge resource with which the user interacts more]. The MLM trainer 136 may adjust the rank by a predetermined amount up or down based on whether the user accepted or rejected, respectively, the ranks 126. The MLM trainer 136 may adjust (at block 504) ranks lower, e.g., by fixed percentage, for answer files 126 in MLM output not in the final modified subset 134.”)
Claim 11, recites limitations analogous to claim 6, so is rejected under the same rationale.
Claim(s) 2 – 4 and 8 – 9 are rejected under 35 U.S.C. 103 as being unpatentable over Lewis in view of Schmirler, Zhu and in further view of Sali et al., Pub. No.: US20180225391A1 and PROKHORENKOVA et al., Pub. No.: US20220405615A1.
Regarding claim 2, Lewis in view of Schmirler and Zhu teach the method of claim 1.
Lewis in view of Schmirler and Zhu do not teach:
wherein: the first prediction algorithm model comprises an algorithm model realized by a logistic regression algorithm or a collaborative filtering algorithm; and the second prediction algorithm model comprises a CTR model realized by gradient boost decision tree algorithm + logistic regression algorithm or gradient boost decision tree algorithm + factorization machine algorithm.
Sail teaches:
wherein: the first prediction algorithm model comprises an algorithm model realized by a logistic regression algorithm or a collaborative filtering algorithm; and
(Sali, “[0052] For example, the possible types of regression algorithms might be Adaboost, Automatic Relevance Determination Regression (ARD)”)… … linear support vector machine, Logistic regression [the first prediction algorithm model comprises an algorithm model realized by a logistic regression algorithm], Multinomial naïve bayes, Neural networks, Passive Aggressive, Quadratic Discriminant Analysis (QDA), …”)
+ factorization machine algorithm.
(Sali, “[0051] The possible types of collaborative filtering algorithms may be Matrix factorization based [factorization machine algorithm] (discussed in the article “Matrix Factorization Techniques for Recommender Systems” by Yehuda Koren, Robert Bell, …”)
Sali, Lewis, Schmirler and Zhu are related to the same field of endeavor (i.e.: training neural network to provide technical solutions). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of Sali with teachings of Lewis, Schmirler and Zhu to coordinate multiple distributed modeling services that evaluate different preprocessing operations and modeling algorithms using validated scores to enable automated selection and tracking of optimized model configuration for generating more accurate technical solutions. (Sali, Abstract).
Lewis in view of Schmirler, Zhu and Sali do not teach:
the second prediction algorithm model comprises a CTR model realized by gradient boost decision tree algorithm + logistic regression algorithm or gradient boost decision tree algorithm
PROKHORENKOVA teaches:
the second prediction algorithm model comprises a CTR model realized by gradient boost decision tree algorithm + logistic regression algorithm or gradient boost decision tree algorithm
(PROKHORENKOVA, “[0006] Embodiments of the present technology have been developed based on developers' appreciation of at least one technical problem associated with the prior art approaches to building Gradient Boosted Decision Tree (GBDT) models [gradient boost decision tree algorithm] and for determining uncertainty of outputs of the GBDT models.”)
PROKHORENKOVA, Lewis, Schmirler, Zhu and Sali are related to the same field of endeavor (i.e.: training neural network to provide technical solutions). It would have been obvious to a person having ordinary skill in the art before the effective filing date of the invention to combine the teaching of PROKHORENKOVA with teachings of Lewis, Schmirler, Zhu and Sali to add uncertainty scoring for machine learning predictions based on variations among outputs of multiple sub models derived from Gradient Boosted Decision tree model to improve confidence assessment and reliability of the technical solutions. (PROKHORENKOVA, Abstract).
Regarding claim 3, Lewis in view of Schmirler, Zhu, Sali and PROKHORENKOVA teach the method of claim 2.
Lewis further teaches: further comprising: before inputting the second number of knowledge items to a knowledge recommendation model, providing the second prediction information including the second number of knowledge items to the user for confirmation, and receiving first feedback information of the user for the second number of knowledge items; and correcting the second prediction information according to the first feedback information to obtain a corrected second number of knowledge items.
(Lewis, “[0032] With the embodiment of FIG. 5, the MLM trainer 136 may generate adjusted ranks 138 for the answer files from the machine learning module 124 ranks 126 based on user feedback 140 [before inputting the second number of knowledge items to a knowledge recommendation model, providing the second prediction information including the second number of knowledge items to the user for confirmation, and receiving first feedback information of the user for the second number of knowledge items] and then retrain the machine learning module 124 to produce the adjusted ranks 126 [and correcting the second prediction information according to the first feedback information to obtain a corrected second number of knowledge items]. In this way, user feedback is used to improve the ranking the machine learning module 124 will output for answer files based on input specific to the user profile and criteria. This training further optimizes the final output subset of answer files 134 by improving the accuracy of the rankings to predict relevancy of solutions/fixes in answer files presented to the user.”)
Claim 8, recites limitations analogous to claim 3, so is rejected under the same rationale.
Regarding claim 4, Lewis in view of Schmirler, Zhu, Sali and PROKHORENKOVA teach the method of claim 3.
Lewis further teaches: further comprising providing the first feedback information or corrected second prediction information to the first prediction algorithm model for learning.
(Lewis, “[0023] FIG. 3 illustrates an embodiment of a historical solution record 300 i in the historical solutions database 300 including information on past received answer files, including: a user identifier 302 for which the solution was provided; all the input 304 to the machine learning module 124 that produced the outputted ranks for answer files 306 for a technical support request; final modified subset of answer files provided to the user 308, such as files 134; user feedback 310 on the ranked answer files provided to the user; and adjusted output of the ranks for the answer files 312, adjusted based on the user feedback 308 [providing the first feedback information or corrected second prediction information]. The adjusted ranks 312 may be adjusted to a rank value specified by the user or adjusted upward or downward by a fixed or variable percentage based on whether the user indicated approval or disapproval of the rank [to the first prediction algorithm model for learning].”)
Claim 9, recites limitations analogous to claim 4, so is rejected under the same rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Bauer et al., Pub. No.: US20210034043A1.
Bauer teaches an automated booking of manual activities carried out by a worker in an industrial manufacturing plant while processing a workpiece is disclosed. The booking is performed in a digital control system for the creation of a digital process chain of the manufacturing.
Principato, Pub. No.: US20200356831A1.
Principato teaches using encoded images on physical objects to trace specifications for a manufacturing process. In one embodiment, specifications for a product (e.g., a non-volatile memory device) made by a manufacturing process are traced by a unified QR code printed on various paper documents that are exchanged among multiple parties involved in the manufacturing process.
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/M.T.M./ Examiner, Art Unit 2148
/MICHELLE T BECHTOLD/ Supervisory Patent Examiner, Art Unit 2148
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