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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/11/2022 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim 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:
Claim
Limitations
Location
1
a messaging module of the explanation engine configured to collect the top-level result
[56-57, 71]
2
a terminology module configured to assign terminology
[44-45]
3
terminology module of the explanation engine configured to accept
[44-45]
3
terminology module is configured to crawl
[44-45]
4
second reasoning engine that is configured to create
[30]
4
explanation engine is configured to cooperate
[19]
5
crawl back module configured to cooperate
[24-26, 37]
6
crawl back module configured to cooperate
[24-26, 37]
6
crawl back module of the explanation engine is configured to crawl through
[24-26, 37]
7
ablation module configured to change
[23, 36, 52]
8
ablation module configured to conduct
[23, 36, 52]
8
messaging module is configured to take results
[56-57, 71]
9
messaging module of the explanation engine is configured to 1) extract… 2) cooperate
[56-57, 71]
14
Same as Claim 4
16
crawl back module of the explanation engine is configured to crawl through
[24-26, 37]
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.
The meaning of the modules is imported from [0094] of the Specification which contains a sufficient combination of computer hardware and algorithms (software) in the structure. The explanation engine contains modules according to [0019] which is sufficient structure. The second reasoning engine contains modules according [0030]. The locations of sufficient algorithmic structure for each of the modules are found in the Location column.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to
an abstract idea without significantly more. The claims recite mental processes and mathematical concepts. This judicial exception is not integrated into a practical application because the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as explained below.
Step 1 for all Claims:
Claims 1-10 are directed to a machine. Claims 11-20 are directed to a method (process). Therefore, Claims 1-20 are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Regarding Claim 1:
Step 2A, Prong 1:
[An apparatus, comprising: an explanation engine having a set of modules cooperating with each other configured to] evaluate layers in a hierarchical architecture of a machine-based reasoning process that uses machine learning to support an explanation of how the machine-based reasoning process arrived at its reported results of both a top-level result as well as corresponding intermediate output results, As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses evaluating layers to provide an explanation which is making an evaluation based upon the inputs and results of the machine-based reasoning process which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2:
An apparatus, comprising: an explanation engine having a set of modules cooperating with each other configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
and a messaging module of the explanation engine configured to collect the top- level result as well as one or more intermediate output results from intermediate layers of the machine-based reasoning process, where multiple layers of reasoning are associated with terminology used in at least one of i) a problem to be solved and ii) a domain pertinent to the problem in order to communicate how the machine-based reasoning process came to its reported results in a communication. This limitation amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
An apparatus, comprising: an explanation engine having a set of modules cooperating with each other configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
and a messaging module of the explanation engine configured to collect the top- level result as well as one or more intermediate output results from intermediate layers of the machine-based reasoning process, where multiple layers of reasoning are associated with terminology used in at least one of i) a problem to be solved and ii) a domain pertinent to the problem in order to communicate how the machine-based reasoning process came to its reported results in a communication. As discussed above, the additional elements of collecting the top-level result and intermediate output results which is recited at a high level of generality and amounts to extra-solution activity of gathering data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
Regarding Claim 2:
Step 2A, Prong 1:
[The apparatus of claim 1, where the explanation engine has a terminology module configured to] assign terminology from any of i) the domain pertinent to the problem and ii) the specific problem to be solved, for the multiple layers in the hierarchical architecture of the machine-based reasoning process supplied from a reasoning engine, where the user is able to understand the results in terms of the specific problem or domain based on the way the communication is generated. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses assigning terminology from a pertinent domain to the reasoning process to help the user to understand the results which is making observations or opinions based on descriptive terminology pertinent to the field of study in order to provide an explanation, which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2:
The apparatus of claim 1, where the explanation engine has a terminology module configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B:
The apparatus of claim 1, where the explanation engine has a terminology module configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding Claim 3:
Step 2A, Prong 1:
and where the terminology module is configured to crawl through the hierarchical architecture of the machine-based reasoning process, to be created by a reasoning engine, and then associate i) the terminology specific to the problem to be solved supplied by the user and/or terminology specific to a relevant subject matter domain with ii) the multiple layers making up the hierarchical architecture of the machine-based reasoning process. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses crawling through the reasoning process and associating terminology to layers in the reasoning process which is making a judgement based upon how well certain terminology match with certain layers in the reasoning process which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2:
The apparatus of claim 1, where the explanation engine has a terminology module of the explanation engine configured to accept input of terminology for the problem to be solved that is supplied by at least one of i) a description of the problem to be solved ii) a description of preferred approach to solve the problem from a user, and iii) a database of known terminology specific to the domain pertinent to the problem, This limitation amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
The apparatus of claim 1, where the explanation engine has a terminology module of the explanation engine configured to accept input of terminology for the problem to be solved that is supplied by at least one of i) a description of the problem to be solved ii) a description of preferred approach to solve the problem from a user, and iii) a database of known terminology specific to the domain pertinent to the problem, As discussed above, the additional elements of accepting terminology which is recited at a high level of generality and amounts to extra-solution activity of gathering data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
Regarding Claim 4:
Step 2A, Prong 1:
break down its machine-based reasoning process into divisible layers that provide intermediary output results to other layers in order to determine the top level result from the machine-based reasoning process; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses breaking down the reasoning process into layers to determine a top-level result which is making an observation and then a judgement based upon mentally categorizing parts of the reasoning process and then arriving at a result by observing the intermediate results which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2:
The apparatus of claim 1, where the explanation engine is configured to cooperate with a first reasoning engine that is configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
as opposed to a second reasoning engine that is configured to create one omnibus neural network that is compiled as a black box that merely outputs its final decision; This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The final decision outputting is recited at a high-level of generality with no detail of the decision-making process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
and where the explanation engine is configured to cooperate with the first reasoning engine to allow a user to query what the intermediary output results are for each layer of the machine-based reasoning process as well as what would happen when the intermediary output results were altered. This limitation amounts to extra-solution activity of gathering data and outputting for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
The apparatus of claim 1, where the explanation engine is configured to cooperate with a first reasoning engine that is configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
as opposed to a second reasoning engine that is configured to create one omnibus neural network that is compiled as a black box that merely outputs its final decision; This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The final decision outputting is recited at a high-level of generality with no detail of the decision-making process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
and where the explanation engine is configured to cooperate with the first reasoning engine to allow a user to query what the intermediary output results are for each layer of the machine-based reasoning process as well as what would happen when the intermediary output results were altered. As discussed above, the additional elements of accepting user queries which is recited at a high level of generality and amounts to extra-solution activity of gathering data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
Regarding Claim 5:
Step 2A, Prong 1:
[The apparatus of claim 1, where the explanation engine has a crawl back module configured to] cooperate with an ablation module to trace through the intermediate layers of the machine-based reasoning process constructed by a reasoning engine to record factors being considered and how important that factor was into arriving at the top-level result from the machine-based reasoning process. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses tracing through the intermediate layers of a reasoning process and making record of important factors which is making observations of the plurality of layers and making note of the importance of the factors which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2:
The apparatus of claim 1, where the explanation engine has a crawl back module configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B:
The apparatus of claim 1, where the explanation engine has a crawl back module configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding Claim 6:
Step 2A, Prong 2:
[The apparatus of claim 1, where the explanation engine has a crawl back module configured to] cooperate with the messaging module, where the crawl back module of the explanation engine is configured to crawl through a decomposition of the machine- based reasoning process to collect and then report the intermediate output results from the multiple layers of the reasoning process to explain the top-level result in terms of the intermediate output results. This limitation amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The apparatus of claim 1, where the explanation engine has a crawl back module configured to … This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B:
The apparatus of claim 1, where the explanation engine has a crawl back module configured to … This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
[The apparatus of claim 1, where the explanation engine has a crawl back module configured to] cooperate with the messaging module, where the crawl back module of the explanation engine is configured to crawl through a decomposition of the machine- based reasoning process to collect and then report the intermediate output results from the multiple layers of the reasoning process to explain the top-level result in terms of the intermediate output results. As discussed above, the additional elements of collecting the top-level result and intermediate output results and reporting them are recited at a high level of generality and amount to extra-solution activity of gathering data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
Regarding Claim 7:
Step 2A, Prong 1:
[The apparatus of claim 1, where the explanation engine has an ablation module configured to] change the intermediate output results from layers of the machine-based reasoning process by altering an input for that layer and then output a new intermediate output result from that layer of the machine-based reasoning process as well as a new top-level result. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses changing inputs for layers to get different intermediate output results which is making a prediction or calculation based upon altering the initial circumstances in a hypothetical scenario which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2:
The apparatus of claim 1, where the explanation engine has an ablation module configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B:
The apparatus of claim 1, where the explanation engine has an ablation module configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding Claim 8:
Step 2A, Prong 1:
conduct one or more ablation cycles to alter an input to a layer of the machine-based reasoning process created by a reasoning engine to determine an effect of that layer on the top-level result and record the effect; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses determining and recording an effect of a layer on the result which is making a judgement and memory based upon observing the differences between the beginning and end of the ablation cycles and considering the input alterations which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2:
The apparatus of claim 1, further comprising: an ablation module configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
and where the messaging module is configured to take results of the ablation cycles and data generated with them in order to generate the reported results of an impact of each layer of machine-based reasoning process in the communication generated by the messaging module. This limitation amounts to extra-solution activity of gathering outputting for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
The apparatus of claim 1, further comprising: an ablation module configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
and where the messaging module is configured to take results of the ablation cycles and data generated with them in order to generate the reported results of an impact of each layer of machine-based reasoning process in the communication generated by the messaging module. As discussed above, the additional elements of taking results and data to generate a report which is recited at a high level of generality and amounts to extra-solution activity of transmitting data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
Regarding Claim 9:
Step 2A, Prong 1:
[The apparatus of claim 1, further comprising: where the messaging module of the explanation engine is configured to] 1) extract the intermediate output results from the multiple layers of the machine-based reasoning process created by a reasoning engine and 2) cooperate with a terminology module to associate the intermediate output results from the multiple layers with the terminology taken from the at least one of i) subject domain pertinent to the problem and ii) the problem specific terminology used in the problem to be solved. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses extracting intermediate results and associating those results with terminology which is making a judgement based upon analyzing the multiple layers of the reasoning process and then matching results to their appropriate terminology which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2:
The apparatus of claim 1, further comprising: where the messaging module of the explanation engine is configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B:
The apparatus of claim 1, further comprising: where the messaging module of the explanation engine is configured to… This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The configuration is recited at a high-level of generality with no detail of the configuration process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding Claim 10:
Step 2A, Prong 1:
causing an explanation engine having a set of modules to evaluate layers in a hierarchical architecture of a machine-based reasoning process that uses machine learning to support an explanation of how the machine-based reasoning process arrived at its reported results of both a top-level result as well as corresponding intermediate output results, As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses evaluating layers to provide an explanation which is making an evaluation based upon the inputs and results of the machine-based reasoning process which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2:
A non-transitory computer-readable medium including executable instructions that, when executed with one or more processors, cause an explanation engine to perform operations as follows, comprising: This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The performing of operations is recited at a high-level of generality with no detail of the operations performance process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
and causing a messaging module of the explanation engine to collect the top-level result as well as one or more intermediate output results from intermediate layers of the machine-based reasoning process, where each layer of reasoning is associated with terminology used in at least one of i) a problem being solved and ii) a domain pertinent to the problem in order to communicate how the machine-based reasoning process came to its reported results in a communication. This limitation amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
A non-transitory computer-readable medium including executable instructions that, when executed with one or more processors, cause an explanation engine to perform operations as follows, comprising: This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The performing of operations is recited at a high-level of generality with no detail of the operations performance process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
and causing a messaging module of the explanation engine to collect the top-level result as well as one or more intermediate output results from intermediate layers of the machine-based reasoning process, where each layer of reasoning is associated with terminology used in at least one of i) a problem being solved and ii) a domain pertinent to the problem in order to communicate how the machine-based reasoning process came to its reported results in a communication. As discussed above, the additional elements of collecting results which is recited at a high level of generality and amounts to extra-solution activity of gathering data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
Regarding Claim 11:
The claim is rejected on the same grounds as Claim 1 for reciting substantially similar limitations.
Regarding Claim 12:
The claim is rejected on the same grounds as Claim 2 for reciting substantially similar limitations.
Regarding Claim 13:
The claim is rejected on the same grounds as Claim 3 for reciting substantially similar limitations.
Regarding Claim 14:
The claim is rejected on the same grounds as Claim 4 for reciting substantially similar limitations.
Regarding Claim 15:
The claim is rejected on the same grounds as Claim 5 for reciting substantially similar limitations.
Regarding Claim 16:
The claim is rejected on the same grounds as Claim 6 for reciting substantially similar limitations.
Regarding Claim 17:
Step 2A, Prong 1:
The method of claim 11, further comprising: configuring an ablation module of the explanation engine to remove each intermediate layer of the machine-based reasoning process, one at a time, and evaluate an impact on the top-level result from the machine-based reasoning process. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses evaluating the impact of layers on the top-level result to provide an explanation which is making an evaluation based upon manipulating the inputs of the machine-based reasoning process which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Regarding Claim 18:
The claim is rejected on the same grounds as Claim 7 for reciting substantially similar limitations.
Regarding Claim 19:
The claim is rejected on the same grounds as Claim 8 for reciting substantially similar limitations.
Regarding Claim 20:
The claim is rejected on the same grounds as Claim 9 for reciting substantially similar limitations.
Just say okClaim 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.
Claims 1-3, 5-13, 15-16, 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Forsyth et al. (US 20200311798 A1) in view of Chatterjee et al. (US 10824959 B1), hereinafter referred to as Forsyth and Chatterjee, respectively.
Regarding Claim 1:
Forsyth teaches: An apparatus, comprising: an explanation engine having a set of modules cooperating with each other configured to evaluate layers in a hierarchical architecture of a machine-based reasoning process… ([0046] “FIG. 4A is a flow chart 400 to illustrate a neural network regression flow use of visual semantic embeddings that employs a two-step training procedure, according to an embodiment. As illustrated, the NN regressor model may be trained with use of a multi-layer neural network. As pre-processing steps, the search engine server 120 receives an input image 410 on which to train, which is submitted to the visual semantic embedder 124 (FIG. 1) in order to generate a visual semantic embedding for the input image 410. The search engine server 120 also receives, as an input, a group of words that represent labels describing the product represented by the input image 410 using the terms in the developed lexicon, which was discussed above. In one embodiment, the NN regressor model computes the visual semantic embedding based on a visual-semantic loss between a text-based image embedding of the input image and features represented within the group of words, as will be discussed in more detail with reference to FIG. 4B.”
Examiner’s Note: The search engine server is read as the explanation engine. The multi-layer neural network is read as hierarchical architecture, especially given FIG. 4A which displays the variety of modules/components organized in order and function. Computing the visual semantic embedding based on visual-semantic loss is read as evaluating layers… in a machine-based reasoning process.
where multiple layers of reasoning are associated with terminology used in at least one of i) a problem to be solved and ii) a domain pertinent to the problem in order to communicate how the machine-based reasoning process came to its reported results in a communication. ([0038] “To train the regressor, a dataset of more than 75,000 fashion products was captured from Net-A-Porter, a popular online fashion retailer, mining the product images and accompanying text descriptions for each item. Through an iterative open encoding of frequently occurring unigrams, bigrams, and trigrams in the text descriptions, the search engine server 120 created a lexicon of 1,300 fashion characteristics broken down into eight categories: type, shape/silhouette, color, pattern/print, material, details/trim, brand, and style/context. Using this dataset, the search engine server 120 trains a two-layer neural network using a regression loss function over all 1,300 characteristics. Then, the search engine server 120 leverages these pre-trained layers to train one additional neuron per characteristic, allowing the disclosed model to capture fashion characteristics with only few representative examples in the training set.”
Examiner’s Note: The search engine is read as the explanation engine, and the visual semantic embedder serves as the terminology module within its respective engine. The terminology in the art is all the collected lexicon of fashion characteristics that come from the fashion domain, such as the 75,000 fashion products from fashion retailer website Net-A-Porter, which is pertinent to the field and problem to be solved. These terminology assignments, AKA the trainings of one neuron per characteristic, are done through a multi-layer neural network to produce explanations.)
Forsyth fails to teach: …as well as one or more intermediate output results from intermediate layers of the machine-based reasoning process…
However, Chatterjee teaches: that uses machine learning to support an explanation of how the machine-based reasoning process arrived at its reported results of both a top-level result as well as corresponding intermediate output results, (Col 5 Lines 47-67 & Col 6 Lines 1-4 “Some classification techniques, such as those based on neural networks, may generate internal representations of the input data or intermediate data structures which are neither part of the input data, nor part of the model output. The predictions made by some such models may sometimes depend more directly on these internal representations (which may sometimes be referred to as “hidden” layers of the model) than on the raw input data values themselves… In such an embodiment, when a client requests an explanation for a particular prediction, a first level explanation may sometimes be provided in terms of the intermediate representations.”
Examiner’s Note: The particular prediction is read as the top-level result. The intermediate representations are read as the intermediate output results.)
and a messaging module of the explanation engine configured to collect the top-level result as well as one or more intermediate output results from intermediate layers of the machine-based reasoning process… (Col 5 Lines 47-67 & Col 6 Lines 1-4 “Some classification techniques, such as those based on neural networks, may generate internal representations of the input data or intermediate data structures which are neither part of the input data, nor part of the model output. The predictions made by some such models may sometimes depend more directly on these internal representations (which may sometimes be referred to as “hidden” layers of the model) than on the raw input data values themselves… In such an embodiment, when a client requests an explanation for a particular prediction, a first level explanation may sometimes be provided in terms of the intermediate representations.”
Examiner’s Note: The particular prediction is read as the top-level result. The intermediate representations are read as the intermediate output results. According to Fig. 1, the Explainer selector 160 collects these results and is read as the messaging module.)
Forsyth and Chatterjee are considered to be analogous to each other as they are all in the field of machine learning. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the explanation engine taught by Forsyth and the messaging module that collects top-level and intermediate results taught by Chatterjee in order to provide easier-to-understand explanations expressed through the rule-mining techniques that indicate relationships between predicates and the results. (Col 15 Lines 53-63 “In response to client requests for explanations, the first-level explainer may provide explanatory rules which indicate relationships between predicates on properties of the internal representations and the classifier's predictions. In the case of neural network classifiers, for example, the predicates of the first-level explanatory rules may be expressed in terms of the weights assigned to connections to or from one or more hidden-layer nodes, the number of input links leading into a given hidden-layer node, the number of output links emanating from a given hidden-layer node, etc.”)
Regarding Claim 2:
Forsyth further teaches: The apparatus of claim 1, where the explanation engine has a terminology module configured to assign terminology from any of i) the domain pertinent… to the problem for the multiple layers in the hierarchical architecture of the machine-based reasoning process supplied from a reasoning engine… ([0038] “To train the regressor, a dataset of more than 75,000 fashion products was captured from Net-A-Porter, a popular online fashion retailer, mining the product images and accompanying text descriptions for each item. Through an iterative open encoding of frequently occurring unigrams, bigrams, and trigrams in the text descriptions, the search engine server 120 created a lexicon of 1,300 fashion characteristics broken down into eight categories: type, shape/silhouette, color, pattern/print, material, details/trim, brand, and style/context. Using this dataset, the search engine server 120 trains a two-layer neural network using a regression loss function over all 1,300 characteristics. Then, the search engine server 120 leverages these pre-trained layers to train one additional neuron per characteristic, allowing the disclosed model to capture fashion characteristics with only few representative examples in the training set.”
Examiner’s Note: The search engine is read as the explanation engine, and the visual semantic embedder serves as the terminology module within its respective engine. The terminology in the art is all the collected lexicon of fashion characteristics that come from the fashion domain, such as the 75,000 fashion products from fashion retailer website Net-A-Porter, which is pertinent to the field and problem to be solved. These terminology assignments, AKA the trainings of one neuron per characteristic, are done through a multi-layer neural network to produce explanations.)
and ii) the specific problem to be solved, ([0031] “Given the abundance of annotated fashion image data that may be found online, previous research has leveraged deep learning techniques to build models that support multi-modal input queries. However, because these models are generally trained on text corpora that are too sparse to capture the complex and evolving semantic relationships that exist in fashion, far less attention has been paid to multi-modal output: generating coherent linguistic justifications for a model's recommendations.”
Examiner’s Note: The general goal of the invention is to satisfy the demand of clients for fashion recommendations in the form of easily understandable explanations. The specific problem to be solved is for the model to capture the complex semantic relationships within fashion within its fashion recommendations which has not been addressed in previous research.)
where the user is able to understand the results in terms of the specific problem or domain based on the way the communication is generated. ([0032] “FIG. 2 illustrates an example of context-based product queries with stylist explanations, powered by a regression model, according to an embodiment.” [0039] “The disclosed method of fashion characteristic extraction is based on a structured lexicon, allowing the search engine server 120 to provide explanations by leveraging different categories of characteristics.”)
Regarding Claim 3:
Forsyth further teaches: The apparatus of claim 1, where the explanation engine has a terminology module of the explanation engine configured to accept input of terminology for the problem to be solved that is supplied by at least one of i) a description of the problem to be solved ii) a description of preferred approach to solve the problem from a user, and iii) a database of known terminology specific to the domain pertinent to the problem, ([0038] “To train the regressor, a dataset of more than 75,000 fashion products was captured from Net-A-Porter, a popular online fashion retailer, mining the product images and accompanying text descriptions for each item. Through an iterative open encoding of frequently occurring unigrams, bigrams, and trigrams in the text descriptions, the search engine server 120 created a lexicon of 1,300 fashion characteristics broken down into eight categories: type, shape/silhouette, color, pattern/print, material, details/trim, brand, and style/context. Using this dataset, the search engine server 120 trains a two-layer neural network using a regression loss function over all 1,300 characteristics. Then, the search engine server 120 leverages these pre-trained layers to train one additional neuron per characteristic, allowing the disclosed model to capture fashion characteristics with only few representative examples in the training set. This architecture also makes the model extensible: as tastes change and fashion evolves, new characteristics can be added without having to retrain the entire network.”
Examiner’s Note: To remain consistent, the search engine (server) is again read as the explanation engine. [0038] satisfies the third requirement; the invention mines and accepts the terminology scraped from the database of Net-A-Porter online retailer which includes more than 75,000 fashion products. Given that the problem is related to fashion recommendations and the online retailer sells fashion-related, they both share the same domain.
and where the terminology module is configured to crawl through the hierarchical architecture of the machine-based reasoning process, to be created by a reasoning engine, and then associate i) the terminology specific to the problem to be solved supplied by the user and/or terminology specific to a relevant subject matter domain with ii) the multiple layers making up the hierarchical architecture of the machine-based reasoning process. ([0046] “FIG. 4A is a flow chart 400 to illustrate a neural network regression flow use of visual semantic embeddings that employs a two-step training procedure, according to an embodiment. As illustrated, the NN regressor model may be trained with use of a multi-layer neural network. As pre-processing steps, the search engine server 120 receives an input image 410 on which to train, which is submitted to the visual semantic embedder 124 (FIG. 1) in order to generate a visual semantic embedding for the input image 410. The search engine server 120 also receives, as an input, a group of words that represent labels describing the product represented by the input image 410 using the terms in the developed lexicon, which was discussed above. In one embodiment, the NN regressor model computes the visual semantic embedding based on a visual-semantic loss between a text-based image embedding of the input image and features represented within the group of words, as will be discussed in more detail with reference to FIG. 4B.”
[0119] “In various embodiments and with additional reference to FIG. 4B, the search engine server 120 begins by encoding each image in a general embedding space, which it uses to measure item similarity. The search engine server 120 (e.g., the visual semantic embedder 124) may then train a general embedding using a visual-semantic loss between the image embedding and features representing a text description of the corresponding item. This helps ensure that semantically similar items are projected in a nearby space.”
Examiner’s Note: The use of a neural regressor (neural network regressor model) is read as crawling back through the layers of the NN model. The visual semantic embedder is read as the terminology module. [0046-0049] goes on to describe how some of the NN regressor layers, specifically those within the vector predictor, are trained (AKA associated) to carry out the previously described function. Therefore, these specific layers are “associated” with the terminology.
Regarding Claim 5:
Forsyth further teaches: The apparatus of claim 1, where the explanation engine has a crawl back module configured to cooperate with an ablation module to trace through the intermediate layers of the machine-based reasoning process constructed by a reasoning engine to record factors being considered and how important that factor was into arriving at the top-level result from the machine-based reasoning process. ([0095] “Using the neural regressor, the search engine server 120 may compute activations for all 1300 neurons for product images. Each activation may represent an “association score” of that term and the item. Users then query the NN regressor model for style and context with arbitrary combinations of these 1300 characteristics. Those queries may be submitted in a variety of different query types.”
[0096] “Text-based queries: To compute how strongly a term, (i), is associated with a product, the search engine server 120 computes the visual semantic embedding of the product and then computes the “association score” for that term using the activation of the i.sup.th neuron. To retrieve a set of (k) items that best match a term of the query, the search engine server 120 samples a subset of fashion items, and returns the top k items with the largest “association score” for the term. If the query contains a set of terms, the search engine server 120 computes the combined association score as the product of the association scores of individual terms, so products with a very low association scores for any term in the set get penalized heavily.”
[0098] “To show and explain results for a text query with style or outfit level terms, the search engine server 120 first computes the top k most “associated” items for the query using the procedure described above. The search engine server 120 may then compute the average activation score for each term in the lexicon for these k products. The top element-level terms with the highest average activation scores serve as explanations for our results, e.g., may be formatted into phrases and sentences that explain the relevance of the image-based search results supplied in response to the query. Such explanations also allow us to better understand outfit or even brand-level styles.”
Examiner’s Note The neural regressor (neural network regressor model) is read as the crawl back module crawling back through the layers of the NN model since they both collects the data from the layers. The terms and every term’s activation score are read as the factors and how important that factor was to arrive at the end result using reasoning, respectively. The items are ranked according each item’s combined association scores to individual terms. Then, the average activation score for each term for the ranked items are computed, the terms with the highest activation becoming the result’s reasoning and most important factors.
[0151] “In Table 7, we provide an ablation study to supplement the results of Table 6.”
Examiner’s Note: Forsyth in [0151-0152] includes an ablation (or an ablation module) that the previously described process operates alongside.
Regarding Claim 6:
Forsyth further teaches: The apparatus of claim 1, where the explanation engine has a crawl back module configured to cooperate with the messaging module, where the crawl back module of the explanation engine is configured to crawl through a decomposition of the machine-based reasoning process to collect… ([0095] “Using the neural regressor, the search engine server 120 may compute activations for all 1300 neurons for product images. Each activation may represent an “association score” of that term and the item. Users then query the NN regressor model for style and context with arbitrary combinations of these 1300 characteristics. Those queries may be submitted in a variety of different query types.”
Examiner’s Note: The neural regressor (neural network regressor model) is read as the crawl back module crawling back through the layers of the NN model since they both collects the data from the layers.)
Forsyth fails to teach: and then report the intermediate output results from the multiple layers of the reasoning process to explain the top-level result in terms of the intermediate output results.
However, Chatterjee teaches: and then report the intermediate output results from the multiple layers of the reasoning process to explain the top-level result in terms of the intermediate output results. (Col 5 Lines 47-67 & Col 6 Lines 1-4 “Some classification techniques, such as those based on neural networks, may generate internal representations of the input data or intermediate data structures which are neither part of the input data, nor part of the model output. The predictions made by some such models may sometimes depend more directly on these internal representations (which may sometimes be referred to as “hidden” layers of the model) than on the raw input data values themselves… In such an embodiment, when a client requests an explanation for a particular prediction, a first level explanation may sometimes be provided in terms of the intermediate representations.”
Examiner’s Note: The particular prediction is read as the top-level result. The intermediate representations are read as the intermediate output results. According to Fig. 1, the Explainer selector 160 collects these results and is read as the messaging module.)
Forsyth and Chatterjee are considered to be analogous to each other as they are all in the field of machine learning. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the explanation engine taught by Forsyth and reporting the collected top-level and intermediate results taught by Chatterjee in order to provide easier-to-understand explanations expressed through the rule-mining techniques that indicate relationships between predicates and the results. (Col 15 Lines 53-63 “In response to client requests for explanations, the first-level explainer may provide explanatory rules which indicate relationships between predicates on properties of the internal representations and the classifier's predictions. In the case of neural network classifiers, for example, the predicates of the first-level explanatory rules may be expressed in terms of the weights assigned to connections to or from one or more hidden-layer nodes, the number of input links leading into a given hidden-layer node, the number of output links emanating from a given hidden-layer node, etc.”)
Regarding Claim 7:
Forsyth further teaches: The apparatus of claim 1, where the explanation engine has an ablation module configured to change the intermediate output results from layers of the machine-based reasoning process by altering an input for that layer and then output a new intermediate output result from that layer of the machine-based reasoning process as well as a new top-level result. ([0151] “More specifically, Table 7 includes… additional ablations including components illustrated in FIG. 4B. In Table 7, we provide an ablation study to supplement the results of Table 6. In further embodiments, additional layers are employed including the FC, which uses a fully connected layer for its type-specific projection rather than a learned diagonal projection, and Cosine, which uses cosine distance to train the NN regressor model rather than Euclidean distance.”
[0152] “The first line of Table 7(b) illustrates that learning our type specific embeddings gives a consistent improvement over training a single embedding to make comparisons. We note that our relative performance using the entire dataset is higher than our disjoint set, which we attribute to likely being due to the additional training data for learning each type-specific embedding. Analogous to the Maryland dataset, the next three lines of Table 7(b) illustrate a consistent performance improvement as we add in the remaining pieces of our model.”
Examiner’s Note: The ablation study tests the different combinations of layers, embeddings, and datasets used. The varying usage of different database sets is read as altering the input data. The varying integrations of layers and embeddings is read as changing the intermediate output from the layers. Furthermore, the table demonstrates that different accuracies result from the removing and adding of layers and datasets; this is read as outputting a new intermediate output result from the layer of the machine-based reasoning process.
Regarding Claim 8:
Forsyth fails to teach: The apparatus of claim 1, further comprising: an ablation module configured to conduct one or more ablation cycles to alter an input to a layer of the machine-based reasoning process created by a reasoning engine to determine an effect of that layer on the top-level result and record the effect; and where the messaging module is configured to take results of the ablation cycles and data generated with them in order to generate the reported results of an impact of each layer of machine-based reasoning process in the communication generated by the messaging module.
However, Chatterjee teaches: The apparatus of claim 1, further comprising: an ablation module configured to conduct one or more ablation cycles to alter an input to a layer of the machine-based reasoning process created by a reasoning engine to determine an effect of that layer on the top-level result and record the effect; (Col 14 Lines 9-19 “Using the contents of table 521, a selected rule mining algorithm has obtained a rule set 525 comprising three rules R0, R1 and R2. Each rule indicates some set of predicates on the input attributes, and an implication about what the classifier would predict regarding the favorite sport if the attribute predicate conditions are met by a given observation record. Two example criteria for ranking the rules are shown: support or coverage (indicative of the fraction of the input data which meets the attribute predicate conditions of the different rules) and confidence or accuracy (indicative of the correctness of the implication).”
Col 15 Lines 43-56 “In an initial stage 720A of a potentially iterative or hierarchical rule-mining technique…”
Examiner’s Note: The changing predicates on the input attributes is read as the altered input into layers of the machine-based reasoning process. The process is potentially iterative. The confidence measure given for each attribute predicate shows how much each attribute predicate’s presence affects prediction, AKA top-level result.)
and where the messaging module is configured to take results of the ablation cycles and data generated with them in order to generate the reported results of an impact of each layer of machine-based reasoning process in the communication generated by the messaging module. (Col 3 Lines 26-33 “For example, the machine learning service may train, test and evaluate a wide variety of models (e.g., for supervised and/or unsupervised learning) in response to client requests received via a set of programmatic interfaces of the service (e.g., application programmatic interfaces or APIs, web-based consoles, command-line tools, or graphical user interfaces.”
Col 15 Lines 43-56 “In an initial stage 720A of a potentially iterative or hierarchical rule-mining technique, an explainer with a rule set 722 formulated in terms of predicates on the properties of the internal representations may be generated. In some cases, the initial rule set may include predicates on the raw input data as well as predicates on the internal representations; that is, the initial rule set may not be restricted to predicates on the properties of the internal representations alone. The initial set of rules may be ranked relative to one another, e.g., using similar types of metrics to those discussed earlier. In response to client requests for explanations, the first-level explainer may provide explanatory rules which indicate relationships between predicates on properties of the internal representations and the classifier's predictions.”
Examiner’s Note: The explainer provides explanatory rules that indicate the relationships between predicates and the predictions. Those results are then provided to the client according to their requests through communication apparatuses.)
Forsyth and Chatterjee are considered to be analogous to each other as they are all in the field of machine learning. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the explanation engine taught by Forsyth and the ablation module that generates the results of each layer’s impact on the reasoning process and the messaging module that reports the results of the ablation module taught by Chatterjee in order to provide easier-to-understand explanations expressed through the rule-mining techniques that indicate relationships between predicates and the prediction results. Here, the rules with predicates are read as the layers. (Col 15 Lines 53-63 “In response to client requests for explanations, the first-level explainer may provide explanatory rules which indicate relationships between predicates on properties of the internal representations and the classifier's predictions. In the case of neural network classifiers, for example, the predicates of the first-level explanatory rules may be expressed in terms of the weights assigned to connections to or from one or more hidden-layer nodes, the number of input links leading into a given hidden-layer node, the number of output links emanating from a given hidden-layer node, etc.”)
Regarding Claim 9:
Forsyth teaches: The apparatus of claim 1, further comprising: where the messaging module of the explanation engine is configured to 1) extract the intermediate output results from the multiple layers of the machine-based reasoning process created by a reasoning engine ([0047] “The multi-layer neural network may further include a first fully connected layer (FC) and rectifier linear unit (ReLU) 420 and a second FC and ReLU 430, which feeds two separate processing layers, namely the complete vector predictor 132 and the individual term predictor 134, which may be executed in parallel in one embodiment. More specifically, the second FC and ReLU 430 (or an additional and final sets of full connected NN layer and rectifier linear unit layers) may output intermediate NN vector outputs that become inputs into the complete vector predictor 132 and the individual term predictor 134.”
Examiner’s Note: To remain consistent, the search engine is again read as the explanation engine. The intermediate NN vector outputs are extracted from the layers of the NN, AKA the FCs and ReLUs. These extractions become inputs for the module in the next part of the reasoning process.)
and 2) cooperate with a terminology module to associate the intermediate output results from the multiple layers with the terminology taken from the at least one of i) subject domain pertinent to the problem and ii) the problem specific terminology used in the problem to be solved. ([0051] “In the second step, the search engine server 120 may stop training with the two hidden layers, e.g., the first and second FC and ReLU layers 420 and 430, and connect the last layer (e.g., the second FC and ReLU layer 430) to 1,300 individual neurons with separate sigmoid activations, where each neuron predicts a specific characteristic term by using the individual term predictor 134… The individual term predictor 134, in order words, includes numerous neurons (e.g., 1300 in this example), each with a corresponding sigmoid activation, where each neuron of the numerous neurons is separately trainable for a respective individual term (e.g., characteristic term) corresponding to the input image.”
Examiner’s Note: The individual term predictor is read as the terminology module since it trains or associates the intermediate output results from the last connected FC and ReLU layers to each neuron with a specific characteristic term. These terms are extracted from fashion websites which are from domains pertinent to the problem being solved; the process of obtaining these terms is outlined in [0042-0044].
Regarding Claim 10:
The claim is rejected on the same grounds as Claim 1 for reciting substantially similar limitations, with the exception of one limitation further taught by Forsyth:
A non-transitory computer-readable medium including executable instructions that, when executed with one or more processors, cause an explanation engine to perform operations as follows, comprising: ([0171] “The computer system 1800 may also include a disk (or optical) drive unit 1815. The disk drive unit 1815 may include a non-transitory computer-readable medium 1840 in which one or more sets of instructions 1802, e.g., software, can be embedded.”)
Regarding Claim 11:
The claim is rejected on the same grounds as Claim 1 for reciting substantially similar limitations.
Regarding Claim 12:
The claim is rejected on the same grounds as Claim 2 for reciting substantially similar limitations.
Regarding Claim 13:
The claim is rejected on the same grounds as Claim 3 for reciting substantially similar limitations.
Regarding Claim 15:
The claim is rejected on the same grounds as Claim 5 for reciting substantially similar limitations.
Regarding Claim 16:
The claim is rejected on the same grounds as Claim 6 for reciting substantially similar limitations.
Regarding Claim 18:
The claim is rejected on the same grounds as Claim 7 for reciting substantially similar limitations.
Regarding Claim 19:
The claim is rejected on the same grounds as Claim 8 for reciting substantially similar limitations.
Regarding Claim 20:
The claim is rejected on the same grounds as Claim 9 for reciting substantially similar limitations.
Claims 4, 14 are rejected under 35 U.S.C. 103 as being unpatentable over Forsyth in view of Chatterjee in further view of Donaldson et al. (US 20220067557 A1), hereinafter referred to as Donaldson.
Regarding Claim 4:
Forsyth further teaches: The apparatus of claim 1, where the explanation engine is configured to cooperate with a first reasoning engine that is configured to break down its machine-based reasoning process into divisible layers that provide intermediary output results to other layers in order to determine the top level result from the machine-based reasoning process; ([0036] “The NN regressor 130 may further include a complete vector predictor 132, an individual term predictor 134, and a mean square loss calculator 136, which will be discussed with reference to FIG. 4A, as part of use of the visual semantic embeddings (vectors) to train the NN regressor 130 to generate fashion-based recommendations and other multi-modals outputs.”
[0047] “The multi-layer neural network may further include a first fully connected layer (FC) and rectifier linear unit (ReLU) 420 and a second FC and ReLU 430, which feeds two separate processing layers, namely the complete vector predictor 132 and the individual term predictor 134, which may be executed in parallel in one embodiment. More specifically, the second FC and ReLU 430 (or an additional and final sets of full connected NN layer and rectifier linear unit layers) may output intermediate NN vector outputs that become inputs into the complete vector predictor 132 and the individual term predictor 134.”
Examiner’s Note: To remain consistent, the search engine is again read as the explanation engine. The multi-layer neural network feeds into two separate processing layers which is read as breaking down its machine-based reasoning process into divisible layers. The two processing layers are used to make fashion-based recommendations which is read as determining the top-level result from the machine-based reasoning process.
Forsyth fails to teach: as opposed to a second reasoning engine that is configured to create one omnibus neural network that is compiled as a black box that merely outputs its final decision;
However, Donaldson teaches: as opposed to a second reasoning engine that is configured to create one omnibus neural network that is compiled as a black box that merely outputs its final decision; ([0013] “A final layer combines the lowest resolution output to make a prediction about the class of the image being considered. The family of Class Activation Mappings (CAM) examine each spatial resolution—particularly the penultimate low-resolution layer—to highlight the areas of the image important to the CNN's classification decision. ’Black box’ methods, such as Randomized Input Sampling for Explanation (RISE), do not require access or knowledge of internal machine learning function processes to highlight influential image regions, though their results are not presented in context of example reference images.”)
Forsyth and Donaldson are considered to be analogous to each other as they are all in the field of machine learning. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the explanation engine taught by Forsyth and the supposed implementation of a “black box” neural network taught by Donaldson in order to not require access or knowledge of the internal machine learning function processes to provide explanations. ([0013] “’Black box’ methods, such as Randomized Input Sampling for Explanation (RISE), do not require access or knowledge of internal machine learning function processes to highlight influential image regions, though their results are not presented in context of example reference images.”)
Forsyth and Donaldson fail to teach: and where the explanation engine is configured to cooperate with the first reasoning engine to allow a user to query what the intermediary output results are for each layer of the machine-based reasoning process as well as what would happen when the intermediary output results were altered.
However, Chatterjee teaches: and where the explanation engine is configured to cooperate with the first reasoning engine to allow a user to query what the intermediary output results are for each layer of the machine-based reasoning process as well as what would happen when the intermediary output results were altered. ([0067] “The respective in-memory database instances may receive the corresponding query execution instructions from the query coordinator. The respective in-memory database instances may execute the corresponding query execution instructions to obtain, process, or both, data (intermediate results data) from the low-latency data. The respective in-memory database instances may output, or otherwise make available, the intermediate results data, such as to the query coordinator.”
[0068] “The query coordinator may execute a respective portion of query execution instructions (allocated to the query coordinator) to obtain, process, or both, data (intermediate results data) from the low-latency data. The query coordinator may receive, or otherwise access, the intermediate results data from the respective in-memory database instances. The query coordinator may combine, aggregate, or otherwise process, the intermediate results data to obtain results data.)”
Forsyth and Chatterjee are considered to be analogous to each other as they are all in the field of machine learning. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the explanation engine taught by Forsyth and the function to allow a user to query the intermediary output results of a reasoning process in order to provide to the user easier-to-understand explanations expressed through the rule-mining techniques that indicate relationships between predicates and the prediction results. Here, the rules with predicates are read as the layers, and the effect of the rules on the prediction outcome is presented to the user. (Col 15 Lines 53-63 “In response to client requests for explanations, the first-level explainer may provide explanatory rules which indicate relationships between predicates on properties of the internal representations and the classifier's predictions. In the case of neural network classifiers, for example, the predicates of the first-level explanatory rules may be expressed in terms of the weights assigned to connections to or from one or more hidden-layer nodes, the number of input links leading into a given hidden-layer node, the number of output links emanating from a given hidden-layer node, etc.”)
Regarding Claim 14:
The claim is rejected on the same grounds as Claim 4 for reciting substantially similar limitations.
Claims 17 is rejected under 35 U.S.C. 103 as being unpatentable over Forsyth in view of Chatterjee in further view of Takeuchi et al. (US 20190221311 A1), hereinafter referred to as Takeuchi.
Regarding Claim 17:
Forsyth fails to teach: The method of claim 11, further comprising: configuring an ablation module of the explanation engine to remove each intermediate layer of the machine-based reasoning process, one at a time, and evaluate an impact on the top-level result from the machine-based reasoning process.
However, Takeuchi teaches: The method of claim 11, further comprising: configuring an ablation module of the explanation engine to remove each intermediate layer of the machine-based reasoning process, one at a time, and evaluate an impact on the top-level result from the machine-based reasoning process. ([0095] “FIG. 5 is a block diagram for illustrating an example of functional components of the neural network NN. The neural network NN includes the input layer 401, the intermediate layer 402, an output layer 403, a conversion module 501, a rearrangement module 502, a prediction data calculation module 503, an importance calculation module 504, a setting module 505, a unification module 506, a dimensionality reduction module 507, and a selection module 508.”
[0103] “The dimensionality reduction module 507 reduces the number d of dimensions of the output vector h.sup.l.sub.D based on the output vector h.sup.l.sub.D from the intermediate layer 402 and the matrix W.sup.l.sub.R as expressed by Expression (2) to output the output vector h.sup.l.sub.R subjected to the dimensionality reduction. The dimensionality reduction module 507 corresponds to the above-mentioned reporting unit group RU. In this case, the setting module 505 sets the weight a for the intermediate layer 402 based on the output vector h.sup.l.sub.R subjected to the dimensionality reduction from the dimensionality reduction module 507 and the matrix W.sub.A.”
[0191] “Further, the analysis apparatus 320 includes the dimensionality reduction module 507, to thereby allow data analysis to become more efficient through dimensionality reduction.
[0135] “The predictive function 910 executes the predictive processing based on the neural network NN (Step S916). Specifically, for example, the predictive function 910 selects the corresponding neural network NN from among the neural network group NNs based on the model parameter MP. Then, the predictive function 910 calculates the prediction result 353 and the importance of the item by supplying the selected neural network NN with the feature vector x.sub.n and the explainable vector and outputs the prediction result 353 and the importance of the item to the explanation function 920 (Step S917).”
Examiner’s Note: The dimensionality reduction module serves as the ablation module and removes each intermediate layer from the output vector. The output vector is then passed on to the analysis apparatus for the predictive function to calculate the prediction result and importance of each item on the final result. The importance of each item is read as the impact of each input of each intermediate layer on the top-level result. Furthermore, [0052] of the Specification states that removing a layer from the reasoning flow by altering an input can be done as a “zero weight”. [0048] of Takeuchi also states the weight of the feature vectors range from 0 to 1.)
Forsyth and Takeuchi are considered to be analogous to each other as they are all in the field of machine learning. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the explanation engine taught by Forsyth and the function to remove intermediate layers of a reasoning process and observe the impact on the top-level result in order to provide to the user easier-to-understand explanations that displays the contribution of a feature to the prediction. ([0018] “This invention has been made in view of the above-mentioned points, and therefore has an object to achieve improvement in interpretability of relevance between a feature that has contributed to a prediction based on machine learning and a clinical pathway.”
[0041] “A normal neural network outputs only the prediction result, but the neural network NN in this embodiment outputs not only the prediction result but also importance of a feature item (hereinafter referred to simply as “item”).”)
Conclusion
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/JOSHUA Y JOO/
Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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