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 .
Claims 1-18 have been canceled. Claims 19-35 have been added. Claims 19-35 have been examined.
Claim Objections
All dependent claims should be grouped together with the claim or claims to which they refer to the extent practicable. In general, applicant's sequence will not be changed. See MPEP § 608.01(m).
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 19
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and determines a connection destination of each input and each output of the plurality of selected trained learners to form a combined learner,
… a processing execution unit that executes the combined learner acquires output data
Claim 20
a combined learner forming unit that receives an instruction of a user … and combines the plurality of selected trained learners
Claim 23
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and combines the plurality of selected trained learners to form a combined learner
Claim 25
a combined learner forming unit that selects a plurality of trained learners … and combines the plurality of selected trained learners
Claim 26
a combined learner forming unit that selects a plurality of trained learners … and combines the plurality of selected trained learners
Claim 28
a display control unit that causes a display to display a combined learner formation interface
Claim 30
a combined learner forming unit that selects a plurality of trained learners … and combines the plurality of selected trained learners
Claim 31
an information providing unit that provides the user with at least one of attribute information
Claim 33
a combined learner forming unit that selects a plurality of trained learners … and combines the plurality of selected trained learners …
an analysis unit that performs analysis relating to the combined learner …
Claim 35
a combined learner forming unit that selects a plurality of trained learners … and determines a connection destination of each input and each output
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 § 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 19-26 and 28-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance (“2019 PEG” — see MPEP 2106.04(II) and 2106.04(d)), requires the examiner to determine if the claims are drawn to one of the statutory categories of invention. Applied to the present application, the claims belong to one of the statutory classes of a machine.
Step 2A of the 2019 PEG is divided into two Prongs.
Step 2A Prong 1 requires the examiner to evaluates whether the claim recites a judicial exception (a law of nature, natural phenomenon, or abstract idea). As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
The relevant claims are copied below, with the limitations belonging to an abstract idea underlined.
Regarding claim 19:
Claim 19 is copied below, with the limitations belonging to an abstract idea underlined.
19. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising:
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and determines a connection destination of each input and each output of the plurality of selected trained learners to form a combined learner,
wherein when the combined learner is executed, a processing execution unit that executes the combined learner acquires output data of a first one of the trained learners constituting the combined learner and inputs the acquired output data to a second one of the trained learners constituting the combined learner, different from the first one of the trained learners.
The broadest reasonable interpretation of the “selects” and “determines” functions is that those functions fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. These limitations recited by the claim therefore amount to a series of mental steps, making these limitations amount to an abstract idea.
Step 2A Prong 2 of the 2019 PEG evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d).
The claim recites the additional elements of a combined learner forming unit and a processing execution unit …
The combined learner forming unit is interpreted to provide mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
The processing execution unit is interpreted to provide performance by a computer which is recited at a high level of generality. Here, the computer is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f).
The claim omits any details as to how the actions solve a technical problem and instead recites only the idea of a solution or outcome. See MPEP 2106.05(f). Therefore, the limitation represents no more than mere instructions to implement the abstract idea, which is equivalent to adding the words “apply it” to the recited judicial exception.
Step 2b of the 2019 PEG requires the examiner to determine whether the additional elements cause the claim to amount to significantly more than the abstract idea itself.
Additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. However, a conclusion that an additional element is insignificant extra-solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g).
As discussed in Step 2A, Prong Two above, the recitations of a combined learner forming unit and a processing execution unit are recited at a high level of generality. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d), subsection II.
As discussed in Step 2A, Prong Two above, the recitation of a computer to perform limitations (a), (b), and (c) amounts to no more than mere instructions to apply the exception using a generic computer component.
Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept.
Therefore, claim 19 is rejected under 35 U.S.C. 101 as directed to an abstract idea without significantly more.
Regarding claim 20:
20. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising:
a combined learner forming unit that receives an instruction of a user indicating a plurality of trained learners selected from the trained learner group, a number of pieces of input data of a combined learner, and the trained learner to be an input destination of each piece of the input data, and combines the plurality of selected trained learners according to the instruction of the user to form the combined learner in which the plurality of pieces of input data of the number designated by user are input to trained learners which differ from each other.
The broadest reasonable interpretation of the “selected” function is that those functions fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. These limitations recited by the claim therefore amount to a series of mental steps, making these limitations amount to an abstract idea. Further analysis is provided with respect to the rejection of claim 19 above.
Regarding claim 21:
21. The combined learner forming apparatus according to claim 19, wherein the combined learner forming unit forms the combined learner by combining the plurality of trained learners including a learner trained by a person other than the user.
The claim recites the additional elements of a learner trained by a person other than the user. These limitations represent mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d). Thus, the claim as a whole fails to integrate the recited judicial exception into a practical application of the exception and fails to amount to significantly more than the recited exception.
Regarding claim 22:
22. The combined learner forming apparatus according to claim 19, wherein the combined learner includes the plurality of trained learners that perform processing using different algorithms.
The claim recites the additional elements of trained learners that perform processing using different algorithms. Under its broadest reasonable interpretation, this element calls for different algorithms but does not place any limits on how the algorithms are implemented. The algorithms could be performed either as described in the disclosure or in other ways known to a person having ordinary skill in the art. As described in ¶ 0036 of Applicant’s disclosure, such algorithms include a neural network, a convolutional neural network, a recurrent neural network, a support vector machine, logistic regression, or random forest, which are considered to be directed to mathematical operations. Further analysis is provided with respect to the rejection of claim 19 above.
Regarding claim 23:
23. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising:
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and combines the plurality of selected trained learners to form a combined learner,
wherein the combined learner forming unit forms a new combined learner by combining the combined learner and a trained learner selected by the user.
Further analysis is provided with respect to the rejection of claim 19 above.
Regarding claim 24:
24. The combined learner forming apparatus according to claim 19, wherein the combined learner forming unit forms the combined learner in which a first learner that is one of the plurality of trained learners and a second learner that is one of the plurality of trained learners and is different from the first learner are connected such that output data of the first learner is directly or indirectly input to the second learner.
The claim recites the additional elements of first and second learners that are connected … These limitations represent mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d). Thus, the claim as a whole fails to integrate the recited judicial exception into a practical application of the exception and fails to amount to significantly more than the recited exception.
Further analysis is provided with respect to the rejection of claim 19 above.
Regarding claim 25:
25. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising:
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and combines the plurality of selected trained learners to form a combined learner,
wherein the combined learner forming unit forms the combined learner in which a first learner that is one of the plurality of trained learners and a second learner that is one of the plurality of trained learners and is different from the first learner are connected such that output data of the first learner is directly or indirectly input to the second learner; the first learner outputs a plurality of pieces of output data; and a part of the plurality of pieces of output data of the first learner is directly or indirectly input to the second learner, and another part of the plurality of pieces of output data of the first learner is output data of the combined learner.
The broadest reasonable interpretation of the “selects” and “forms” functions is that those functions fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. These limitations recited by the claim therefore amount to a series of mental steps, making these limitations amount to an abstract idea.
The claim recites the additional elements of the combined learner forming unit … first learner … and second learner … These limitations represent mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d). Thus, the claim as a whole fails to integrate the recited judicial exception into a practical application of the exception and fails to amount to significantly more than the recited exception.
Further analysis is provided with respect to the rejection of claim 19 above.
Regarding claim 26:
26. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising:
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and combines the plurality of selected trained learners to form a combined learner,
wherein the combined learner forming unit forms the combined learner in which a first learner that is one of the plurality of trained learners and a second learner that is one of the plurality of trained learners and is different from the first learner are connected such that output data of the first learner is directly or indirectly input to the second learner;
the second learner outputs a plurality of pieces of output data; and
a part of the plurality of pieces of output data of the second learner is fed back and input to the first learner.
The claim recites the additional elements of the second learner outputs … fed back and input to the first learner. These limitations represent mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d). Thus, the claim as a whole fails to integrate the recited judicial exception into a practical application of the exception and fails to amount to significantly more than the recited exception. Further analysis is provided with respect to the rejections of claims 19 and 24 above.
Regarding claim 28:
28. The combined learner forming apparatus according to claim 19, further comprising: a display control unit that causes a display to display a combined learner formation interface capable of determining a combined structure of the combined learner by combining learner icons corresponding to the plurality of trained learners, wherein the combined learner forming unit determines a combined structure of the combined learner according to an operation of the user on the combined learner formation interface.
The claim recites the additional elements of a display control unit … The recited computer is recited at a high level of generality, i.e., as a generic computer display performing generic computer display functions and used as a tool to perform the generic computer function of data display. Also, the combined learner forming unit is used to perform an abstract idea such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Thus, the claim as a whole fails to integrate the recited judicial exception into a practical application of the exception and fails to amount to significantly more than the recited exception.
Regarding claim 29:
29. The combined learner forming apparatus according to claim 28, wherein the display control unit displays a warning on the combined learner formation interface in a case where input data is not input to at least one of inputs of the trained learners included in the combined learner.
These limitations represent mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d). Thus, the claim as a whole fails to integrate the recited judicial exception into a practical application of the exception and fails to amount to significantly more than the recited exception.
Regarding claim 30:
30. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising:
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and combines the plurality of selected trained learners to form a combined learner,
wherein the combined learner forming unit forms the combined learner in which a first learner that is one of the plurality of trained learners and a second learner that is one of the plurality of trained learners and is different from the first learner are connected such that output data of the first learner is directly or indirectly input to the second learner;
the combined learner forming apparatus further comprises a display control unit that causes a display to display an input interface for inputting an input name of each of inputs of the plurality of trained learners and an output name of each of outputs of the plurality of trained learners; and
in a case where an output name of an output of the first learner input by the user corresponds to an input name of an input of the second learner input by the user, the combined learner forming unit connects the output of the first learner and the input of the second learner.
Analysis is provided above with respect to claims 19 and 24. The claim as a whole fails to integrate the recited judicial exception into a practical application of the exception and fails to amount to significantly more than the recited exception.
Regarding claim 31:
31. The combined learner forming apparatus according to claim 19, further comprising: an information providing unit that provides the user with at least one of attribute information regarding the trained learners acquired from a person who has caused the trained learners to learn or learning data information regarding learning data obtained when the trained learners have been caused to learn.
The claim recites the additional elements of an information providing unit. These limitations represent mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d). Thus, the claim as a whole fails to integrate the recited judicial exception into a practical application of the exception and fails to amount to significantly more than the recited exception.
Regarding claim 32:
32. The combined learner forming apparatus according to claim 31, wherein the attribute information includes at least one of information regarding preprocessing to be performed on the input data before the input data is input to the trained learners, a program language used for development of the trained learners, or a library used for the development of the trained learners.
The element amounts to mere data gathering. These limitations represent mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. These elements amount to receiving or transmitting data over a network and are well-understood, routine, conventional activity. See MPEP 2106.05(d). Thus, the claim as a whole fails to integrate the recited judicial exception into a practical application of the exception and fails to amount to significantly more than the recited exception.
Regarding claim 33:
33. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising:
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and combines the plurality of selected trained learners to form a combined learner; and
an analysis unit that performs analysis relating to the combined learner, the analysis being performed such that learning processing of each of the trained learners included in the combined learner does not need to be re-executed.
The broadest reasonable interpretation of the “selects” and “analysis” functions is that those functions fall within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. These limitations recited by the claim therefore amount to a series of mental steps, making these limitations amount to an abstract idea.
The analysis unit is recited at a high level of generality and is used as a tool to perform the generic computer function of data analysis. See MPEP 2106.05(f). The analysis unit is used to perform an abstract idea such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). This additional element is at best mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f). Further analysis is provided above with respect to claim 19. Thus, the claim as a whole fails to integrate the recited judicial exception into a practical application of the exception and fails to amount to significantly more than the recited exception.
Regarding claim 34:
34. The combined learner forming apparatus according to claim 33, wherein the analysis unit executes at least one of
sensitivity analysis for analyzing an effect of each of a plurality of pieces of input data of the combined learner on output data of the combined learner,
input optimization processing for searching for input data of the combined learner by a genetic algorithm to obtain optimum output data of the combined learner for the user, or
output range search processing for searching for a possible range of the output data of the combined learner based on a plurality of pieces of input data in a predetermined range by Monte Carlo simulation.
The recitation of sensitivity analysis falls within the mental process groupings of abstract ideas because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion. See MPEP 2106.04(a)(2), subsection III. These limitations recited by the claim therefore amount to a series of mental steps, making these limitations amount to an abstract idea.
The recitations of input optimization processing and output range search processing include limitations of genetic algorithm and Monte Carlo simulation which are understood by a person of ordinary skill in the art as mathematical operations. Therefore, the claim recites a mathematical calculation, which falls within the mathematical concept grouping of abstract ideas. No additional elements are provided. Thus, the claim as a whole fails to integrate the recited judicial exception into a practical application of the exception and fails to amount to significantly more than the recited exception.
Regarding claim 35:
35. A non-transitory computer-readable recording medium storing a combined learner forming program for causing a computer that can access a trained learner group to function as:
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and determines a connection destination of each input and each output of the plurality of selected trained learners to form a combined learner,
wherein when the combined learner is executed, a processing execution unit that executes the combined learner acquires output data of a first one of the trained learners constituting the combined learner, and inputs the acquired output data to a second one of the trained learners constituting the combined learner, different from the first one of the trained learners.
Further analysis is provided with respect to the rejection of claim 19 above. The claim as a whole fails to integrate the recited judicial exception into a practical application of the exception and fails to amount to significantly more than the recited exception.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 19-24, 26, 31 and 33-35 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. Patent Application Publication 20150371133 by Iso (“Iso”).
Regarding claim 19, Iso discloses:
19. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising: See Iso, Fig. 2, depicting an apparatus.
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and determines a connection destination of each input and each output of the plurality of selected trained learners to form a combined learner, Iso, ¶ 0026, “In the example in FIG. 1, first, the sellers operate the seller terminals 10 and provide the providing device 100 with learning devices, in which neurons that output results of calculations on input data are connected and which extract features corresponding to predetermined types from the input data.” Also ¶ 0028, “Thereafter, the providing device 100 generates a learning device NN by adding an additional node NP (corresponding to an example of a new node) of the D company being the buyer to the learning device NA registered by a registration unit 131, …” Also ¶ 0030, “As a specific example, the providing device 100 causes connection coefficients (coupling coefficients) between nodes contained in the learning device NA to be disclosed to only the A company and causes connection coefficients between the additional node NP and other nodes to be disclosed to only the D company among connection coefficients between nodes in the learning device NN.” Also see Fig. 6, depicting selection of a plurality of trained learners.
wherein when the combined learner is executed, a processing execution unit that executes the combined learner acquires output data of a first one of the trained learners constituting the combined learner and inputs the acquired output data to a second one of the trained learners constituting the combined learner, different from the first one of the trained learners. Iso, ¶ 0030, “Further, when the D company uses the learning device NN, the providing device 100 constructs the learning device NN by using the connection coefficients between the nodes contained in the learning device NA and the connection coefficients between the additional node NP and the other nodes, inputs the input data accepted from the A company to the learning device NN, and notifies the P company of an output of the learning device NN.”
Regarding claim 20, Iso discloses:
20. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising: See Iso, Fig. 2, depicting an apparatus.
a combined learner forming unit that receives an instruction of a user indicating a plurality of trained learners selected from the trained learner group, a number of pieces of input data of a combined learner, and the trained learner to be an input destination of each piece of the input data, and combines the plurality of selected trained learners according to the instruction of the user to form the combined learner Iso, ¶ 0026, “In the example in FIG. 1, first, the sellers operate the seller terminals 10 and provide the providing device 100 with learning devices, in which neurons that output results of calculations on input data are connected and which extract features corresponding to predetermined types from the input data.” Also ¶ 0028, “Thereafter, the providing device 100 generates a learning device NN by adding an additional node NP (corresponding to an example of a new node) of the D company being the buyer to the learning device NA registered by a registration unit 131, …” Also ¶ 0030, “As a specific example, the providing device 100 causes connection coefficients (coupling coefficients) between nodes contained in the learning device NA to be disclosed to only the A company and causes connection coefficients between the additional node NP and other nodes to be disclosed to only the D company among connection coefficients between nodes in the learning device NN.” Also see Fig. 6, depicting selection of a plurality of trained learners.
in which the plurality of pieces of input data of the number designated by user are input to trained learners which differ from each other. Iso, e.g. see Fig. 6 depicting multiple difference trained learners.
Regarding claim 21, Iso also discloses:
21. The combined learner forming apparatus according to claim 19, wherein the combined learner forming unit forms the combined learner by combining the plurality of trained learners including a learner trained by a person other than the user. Iso, ¶ 0026, e.g. “For example, an employee of the A company operates the seller terminal 10A and provides the providing device 100 with a learning device NA that is a trained DNN that extracts a feature of “clothes manufactured by the A company”. Similarly, an employee of the B company operates the seller terminal 10B and provides the providing device 100 with a learning device NB that is a trained DNN that extracts a feature of “female”.”
Regarding claim 22, Iso also discloses:
22. The combined learner forming apparatus according to claim 19, wherein the combined learner includes the plurality of trained learners that perform processing using different algorithms. Iso, ¶ 0026 as cited above. Note that the separately trained learning devices will inherently be implemented differently according to a broad interpretation of “different algorithms.”
Regarding claim 23, Iso discloses:
23. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising: See Iso, Fig. 2, depicting an apparatus.
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and combines the plurality of selected trained learners to form a combined learner, Iso, ¶ 0026, “In the example in FIG. 1, first, the sellers operate the seller terminals 10 and provide the providing device 100 with learning devices, in which neurons that output results of calculations on input data are connected and which extract features corresponding to predetermined types from the input data.” Also ¶ 0028, “Thereafter, the providing device 100 generates a learning device NN by adding an additional node NP (corresponding to an example of a new node) of the D company being the buyer to the learning device NA registered by a registration unit 131, …” Also ¶ 0030, “As a specific example, the providing device 100 causes connection coefficients (coupling coefficients) between nodes contained in the learning device NA to be disclosed to only the A company and causes connection coefficients between the additional node NP and other nodes to be disclosed to only the D company among connection coefficients between nodes in the learning device NN.” Also see Fig. 6, depicting selection of a plurality of trained learners.
wherein the combined learner forming unit forms a new combined learner by combining the combined learner and a trained learner selected by the user. See Iso, Figs. 1 and 6, whereby a new combined learner is formed by including learning device NB along with combined learners NA and NN.
Regarding claim 24, Iso also discloses:
24. The combined learner forming apparatus according to claim 19, wherein the combined learner forming unit forms the combined learner in which a first learner that is one of the plurality of trained learners and a second learner that is one of the plurality of trained learners and is different from the first learner are connected such that output data of the first learner is directly or indirectly input to the second learner. See Iso, Fig. 6, depicting output to learning device NN.
Regarding claim 26, Iso discloses:
26. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising: See Iso, Fig. 2, depicting an apparatus.
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and combines the plurality of selected trained learners to form a combined learner, Iso, ¶ 0026, “In the example in FIG. 1, first, the sellers operate the seller terminals 10 and provide the providing device 100 with learning devices, in which neurons that output results of calculations on input data are connected and which extract features corresponding to predetermined types from the input data.” Also ¶ 0028, “Thereafter, the providing device 100 generates a learning device NN by adding an additional node NP (corresponding to an example of a new node) of the D company being the buyer to the learning device NA registered by a registration unit 131, …” Also ¶ 0030, “As a specific example, the providing device 100 causes connection coefficients (coupling coefficients) between nodes contained in the learning device NA to be disclosed to only the A company and causes connection coefficients between the additional node NP and other nodes to be disclosed to only the D company among connection coefficients between nodes in the learning device NN.” Also see Fig. 6, depicting selection of a plurality of trained learners.
wherein the combined learner forming unit forms the combined learner in which a first learner that is one of the plurality of trained learners and a second learner that is one of the plurality of trained learners and is different from the first learner are connected such that output data of the first learner is directly or indirectly input to the second learner; See Iso, Fig. 6, depicting output to learning device NN.
the second learner outputs a plurality of pieces of output data; and a part of the plurality of pieces of output data of the second learner is fed back and input to the first learner. Iso, ¶ 0098, “] In this manner, the providing device 100 provides a learning device in which an error between an input and an output in the integrated learning device is corrected by the backpropagation method. Therefore, the providing device 100 can provide a learning device, in which an error between an input and an output is reduced and which has high discrimination accuracy.”
Regarding claim 31, Iso also discloses:
31. The combined learner forming apparatus according to claim 19, further comprising: an information providing unit that provides the user with at least one of attribute information regarding the trained learners acquired from a person who has caused the trained learners to learn or learning data information regarding learning data obtained when the trained learners have been caused to learn. Iso, ¶ 0026, “Therefore, the providing device 100 registers the learning devices NA to NC provided by the A company to the C company (Step S1).”
Regarding claim 33, Iso discloses:
33. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising: See Iso, Fig. 2, depicting an apparatus.
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and combines the plurality of selected trained learners to form a combined learner; and Iso, ¶ 0026, “In the example in FIG. 1, first, the sellers operate the seller terminals 10 and provide the providing device 100 with learning devices, in which neurons that output results of calculations on input data are connected and which extract features corresponding to predetermined types from the input data.” Also ¶ 0028, “Thereafter, the providing device 100 generates a learning device NN by adding an additional node NP (corresponding to an example of a new node) of the D company being the buyer to the learning device NA registered by a registration unit 131, …” Also ¶ 0030, “As a specific example, the providing device 100 causes connection coefficients (coupling coefficients) between nodes contained in the learning device NA to be disclosed to only the A company and causes connection coefficients between the additional node NP and other nodes to be disclosed to only the D company among connection coefficients between nodes in the learning device NN.” Also see Fig. 6, depicting selection of a plurality of trained learners.
an analysis unit that performs analysis relating to the combined learner, the analysis being performed such that learning processing of each of the trained learners included in the combined learner does not need to be re-executed. Iso, Fig. 7 and ¶ 0079, “Then, the providing device 100 corrects coupling coefficients between neurons such that an error between an input and an output can be reduced as much as possible in the learning device NN in which the learning device NA and the learning device NB are integrated.”
Regarding claim 34, Iso also discloses:
34. The combined learner forming apparatus according to claim 33, wherein the analysis unit executes at least one of sensitivity analysis for analyzing an effect of each of a plurality of pieces of input data of the combined learner on output data of the combined learner, input optimization processing for searching for input data of the combined learner by a genetic algorithm to obtain optimum output data of the combined learner for the user, or output range search processing for searching for a possible range of the output data of the combined learner based on a plurality of pieces of input data in a predetermined range by Monte Carlo simulation. Iso, Fig. 7 and ¶ 0079, “Then, the providing device 100 corrects coupling coefficients between neurons such that an error between an input and an output can be reduced as much as possible in the learning device NN in which the learning device NA and the learning device NB are integrated.” Here, Iso discloses optimization processing since optimum output data can be obtained based upon the input provided.
Regarding claim 35, Iso discloses:
35. A non-transitory computer-readable recording medium storing a combined learner forming program for causing a computer that can access a trained learner group to function as: See Iso, Fig. 10, elements 1200, 1300, and 1400 along with e.g. ¶ 0133, “The HDD 1400 stores therein a program executed by the CPU 1100 and data or the like used by the program.”
All further limitations of claim 35 are addressed in the rejection of claim 19 above.
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) 25 and 28-29 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iso in view of U.S. Patent Application Publication 20210055915 by Guo et al. (“Guo”).
Regarding claim 25, Iso discloses:
25. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising: See Iso, Fig. 2, depicting an apparatus.
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and combines the plurality of selected trained learners to form a combined learner, Iso, ¶ 0026, “In the example in FIG. 1, first, the sellers operate the seller terminals 10 and provide the providing device 100 with learning devices, in which neurons that output results of calculations on input data are connected and which extract features corresponding to predetermined types from the input data.” Also ¶ 0028, “Thereafter, the providing device 100 generates a learning device NN by adding an additional node NP (corresponding to an example of a new node) of the D company being the buyer to the learning device NA registered by a registration unit 131, …” Also ¶ 0030, “As a specific example, the providing device 100 causes connection coefficients (coupling coefficients) between nodes contained in the learning device NA to be disclosed to only the A company and causes connection coefficients between the additional node NP and other nodes to be disclosed to only the D company among connection coefficients between nodes in the learning device NN.” Also see Fig. 6, depicting selection of a plurality of trained learners.
wherein the combined learner forming unit forms the combined learner in which a first learner that is one of the plurality of trained learners and a second learner that is one of the plurality of trained learners and is different from the first learner are connected such that output data of the first learner is directly or indirectly input to the second learner; See Iso, Fig. 6, depicting output to learning device NN.
Iso does not expressly disclose: the first learner outputs a plurality of pieces of output data; and a part of the plurality of pieces of output data of the first learner is directly or indirectly input to the second learner, and another part of the plurality of pieces of output data of the first learner is output data of the combined learner. This is taught by Guo. See Guo, Fig. 3, depicting output of “Training Algorithm I” as an output of the combined “Wrapper 304” as well as output to the “Evaluate” element. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Guo’s outputs with Iso’s learners in order to provide an output to multiple destinations for further review and processing as essentially suggested by Guo.
Regarding claim 28, Iso also discloses:
28. The combined learner forming apparatus according to claim 19, further comprising: a display control unit that causes a display to display a combined learner formation interface capable of determining a combined structure of the combined learner See Iso, ¶ 0134, “The CPU 1100 controls an output device, such as a display … Further, the CPU 1100 outputs generated data to the output device via the input output interface 1600.”
Iso does not expressly disclose: by combining learner icons corresponding to the plurality of trained learners, wherein the combined learner forming unit determines a combined structure of the combined learner according to an operation of the user on the combined learner formation interface. This is taught by Guo. See Fig. 2 depicting a display with icons for operating a design interface. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Guo’s interface with Iso’s display in order to quickly and easily generate, execute and publish machine learning models as suggested by Guo (see ¶ 0022).
Regarding claim 29, Iso does not expressly disclose:
29. The combined learner forming apparatus according to claim 28, wherein the display control unit displays a warning on the combined learner formation interface in a case where input data is not input to at least one of inputs of the trained learners included in the combined learner. This is taught by Guo. See Guo, ¶ 0015, “… the run button is enabled for selection when a machine learning model input dataset is selected.” Also ¶ 0056, “However, in some implementations the run button 220 may be disabled, e.g., if the generated machine learning model in the editing area is missing a routine or otherwise cannot be executed. In these implementations the GUI may also present a warning sign, e.g., in the editing area 210 or next to the run button 220.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Guo’s interface with Iso’s display in order to quickly and easily generate, execute and publish machine learning models as suggested by Guo (see ¶ 0022).
Claim(s) 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iso in view of U.S. Patent Application Publication 20180349757 by Ando (“Ando”).
Regarding claim 27, Iso does not expressly disclose:
27. The combined learner forming apparatus according to claim 24, wherein the combined learner forming unit forms the combined learner including a transformation model that transforms output data of the first learner and is connected to the second learner such that the transformed output data is input to the second learner. This is taught by Ando. See Ando, ¶ 0079, “… converts the data format of the processed data so as to be compatible with the ability unit 502 …” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Ando’s data conversion with Iso’s learner data in order to provide compatible data as suggested by Ando.
Claim(s) 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iso in view of U.S. Patent Application Publication 20210248447 by Rickard et al. (“Rickard”).
Regarding claim 30, Iso discloses:
30. A combined learner forming apparatus capable of accessing a trained learner group, the combined learner forming apparatus comprising: See Iso, Fig. 2, depicting an apparatus.
a combined learner forming unit that selects a plurality of trained learners from the trained learner group according to an instruction of a user and combines the plurality of selected trained learners to form a combined learner, Iso, ¶ 0026, “In the example in FIG. 1, first, the sellers operate the seller terminals 10 and provide the providing device 100 with learning devices, in which neurons that output results of calculations on input data are connected and which extract features corresponding to predetermined types from the input data.” Also ¶ 0028, “Thereafter, the providing device 100 generates a learning device NN by adding an additional node NP (corresponding to an example of a new node) of the D company being the buyer to the learning device NA registered by a registration unit 131, …” Also ¶ 0030, “As a specific example, the providing device 100 causes connection coefficients (coupling coefficients) between nodes contained in the learning device NA to be disclosed to only the A company and causes connection coefficients between the additional node NP and other nodes to be disclosed to only the D company among connection coefficients between nodes in the learning device NN.” Also see Fig. 6, depicting selection of a plurality of trained learners.
wherein the combined learner forming unit forms the combined learner in which a first learner that is one of the plurality of trained learners and a second learner that is one of the plurality of trained learners and is different from the first learner are connected such that output data of the first learner is directly or indirectly input to the second learner; See Iso, Fig. 6, depicting output to learning device NN.
Iso does not expressly disclose the following limitations which are taught by Rickard:
the combined learner forming apparatus further comprises a display control unit that causes a display to display an input interface for inputting an input name of each of inputs of the plurality of trained learners and an output name of each of outputs of the plurality of trained learners; and Rickard, ¶ 0110, “In some embodiments, a user of the system uses an object and property type editor 506 to create and/or modify the object and/or property types (e.g., modify selected 512 compressor 510C configuration) and define attributes of the object types and/or property types (e.g., set the speed, suction pressure, type of compressor etc. for selected 512 compressor 510C). In an embodiment, a user of the system uses a schematic editor 508 to add, delete, move, or edit links, model specific objects, and/or subsystems within the schematic view. Alternatively, other programs, processes, or programmatic controls (e.g., artificial intelligence training system, model connector, model simulator, subsystem simulator and object simulator) may be used to modify, define, add, delete, move, or edit property types, attributes, links, model specific objects and/or subsystems (e.g., using editors may not be required)”
in a case where an output name of an output of the first learner input by the user corresponds to an input name of an input of the second learner input by the user, the combined learner forming unit connects the output of the first learner and the input of the second learner. See Rickard, ¶ 0075, “In an embodiment, a link between a parameter output node of one model and parameter input node of another model may be established by the model connector 143 based on similar or matching parameter output nodes and parameter input nodes (e.g., the parameter output node of one model matches the parameter input node of another model), where the nodes may include model specific data comprised of subsystems, objects, and/or object properties (e.g., property types and/or property values). For example, the artificial intelligence training system 141 may use an RNN to accurately and recurrently classify the nodes by using the nodes as training examples. The nodes may then link and/or re-link based on which classified parameter input nodes and parameter output nodes are most similar.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Rickard’s node matching with learning devices in order to assist a user as suggested by Rickard (see ¶ 0008).
Claim(s) 32 is/are rejected under 35 U.S.C. 103 as being unpatentable over Iso in view of U.S. Patent Application Publication 20210124739 by Karanasos et al. (“Karanasos”).
Regarding claim 32, Iso does not expressly disclose:
32. The combined learner forming apparatus according to claim 31, wherein the attribute information includes at least one of information regarding preprocessing to be performed on the input data before the input data is input to the trained learners, a program language used for development of the trained learners, or a library used for the development of the trained learners. This is taught by Karanasos. See Karanasos ¶ 0066, “In some implementations, the input script(s) can be accompanied by metadata to specify the required runtimes and dependencies (e.g., Python version, libraries used), and to access the referenced data and models.” It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use Karanasos’ dependency specification with Iso’s learning devices in order to communicate requirements as suggested by Karanasos.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Arnaldo et al., "Bring Your Own Learner: A Cloud-Based, Data-Parallel Commons for Machine Learning" Teaches storage of learners in the cloud for ensemble learning (see Fig. 1 on p. 3).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to James D Rutten whose telephone number is (571)272-3703. The examiner can normally be reached M-F 9:00-5:30 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li B Zhen can be reached at (571)272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/James D. Rutten/Primary Examiner, Art Unit 2121
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