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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. 2022-198909, filed on December 13, 2022.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on April 2, 2024 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The disclosure is objected to because of the following informalities:
In paragraph 048, line 2, “is supposed to have are made” should read “is supposed to have been made.”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1-7 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-5 and 7 are device claims. Claim 6 is a method claim. Therefore, the claims are directed to either a process, machine, manufacture or composition of matter.
Claim 1:
Regarding claim 1, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “a non-transitory computer-readable recording medium.” This is a device or machine, which is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
i. “specifying a first plurality of sets of stakeholders that have relationships with each other, based on a configuration of the stakeholders of an AI system” (this is a mental process; a user can define sets of stakeholders given certain criteria, see MPEP § 2106.04(a)(2)(III)),
ii. “comparing the first plurality of sets with a second plurality of sets of stakeholders determined based on the configuration of the stakeholders of another AI system” (this is a mental process, a person could mentally evaluate comparing one plurality of sets with another based on the configuration of stakeholders of an AI system – see MPEP § 2106.04(a)(2)(III))
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claims “recite” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
iii. “a non-transitory computer-readable recording medium storing an artificial intelligence (AI) system check program for causing a computer to execute processing comprising” (mere instructions to apply an exception using a generic computer – see MPEP § 2106.05(f))
iv. “outputting a first set included in the first plurality of sets but not included in the second plurality of sets, as difference information, based on a result of the comparing” (insignificant extra-solution activity to apply the judicial exception – see MPEP § 2106.05(g))
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional element iii recites mere instructions to apply an exception using a generic computer. In addition, additional element iv recites insignificant extra solution activity, which is well-understood, routine, conventional activity of presenting offers and gathering statistics, see OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
Consider the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 2:
Regarding claim 2, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 2 recites the following additional elements:
i. “The non-transitory computer-readable recording medium according to claim 1, the processing further comprising: outputting a second set included in the second plurality of sets but not included in the first plurality of sets, as the difference information, based on the result of the comparing.” (In step 2A, prong 2, this is considered insignificant extra-solution activity, see MPEP § 2106.05(g)). In step 2B, this is also considered insignificant extra solution activity, which is well-understood, routine, conventional activity of presenting offers and gathering statistics, see OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 3:
Regarding claim 3, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 3 recites the following additional elements:
i. “The non-transitory computer-readable recording medium according to claim 1, wherein the first plurality of sets is determined based on the configuration of the stakeholders of the AI system as a template, and the second plurality of sets is determined based on the configuration of the stakeholders of the another AI system as a project.” (this is a mental process, a person could determine what constitutes the two pluralities of sets, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 4:
Regarding claim 4, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 4 recites the following additional elements:
i. “The non-transitory computer-readable recording medium according to claim 1, the processing further comprising: choosing one set of the first plurality of sets from among two or more sets of the first plurality of sets, based on similarity between a document about the two or more sets of the first plurality of sets included in the AI system and the document included in the another AI system.” (this is a mental process, a person could choose sets based on these criteria, see MPEP § 2106.04(a)(2)(III)),
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, than it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 5:
Regarding claim 5, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis to claim 1. Further, claim 5 recites the following additional elements:
i. “The non-transitory computer-readable recording medium according to claim 1, the processing further comprising: reflecting the difference information in the another AI system.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using a generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using a generic computer – see MPEP § 2106.05(f)).
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Claim 6:
Regarding claim 6, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “an artificial intelligence (AI) system check method implemented by a computer.” This is a method, which is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
i. “specifying a first plurality of sets of stakeholders that have relationships with each other, based on the configuration of the stakeholders of an AI system” (this is a mental process; a user can define sets of stakeholders given certain criteria, see MPEP § 2106.04(a)(2)(III)),
ii. “comparing the first plurality of sets with a second plurality of sets of stakeholders determined based on the configuration of the stakeholders of another AI system” (this is a mental process, a person could mentally evaluate comparing one plurality of sets with another based on the configuration of stakeholders of an AI system – see MPEP § 2106.04(a)(2)(III))
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
iii. “an artificial intelligence (AI) system check method implemented by a computer, the method comprising” (mere instructions to apply an exception using a generic computer – see MPEP § 2106.05(f))
iv. “outputting a first set included in the first plurality of sets but not included in the second plurality of sets, as difference information, based on a result of the comparing” (insignificant extra-solution activity to apply the judicial exception – see MPEP § 2106.05(g))
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, element iii recites mere instructions to apply the exception using a generic computer. In addition, additional element iv recites insignificant extra solution activity, which is well-understood, routine, conventional activity of presenting offers and gathering statistics, see OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
Consider the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim 7:
Regarding claim 7, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “an information processing apparatus.” This is a device or machine, which is one of the four statutory categories of invention.
In step 2A prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
i. “specifying a first plurality of sets of stakeholders that have relationships with each other, based on a configuration of the stakeholders of an AI system” (this is a mental process; a user can define sets of stakeholders given certain criteria, see MPEP § 2106.04(a)(2)(III)),
ii. “comparing the first plurality of sets with a second plurality of sets of stakeholders determined based on the configuration of the stakeholders of another AI system” (this is a mental process, a person could mentally evaluate comparing one plurality of sets with another based on the configuration of stakeholders of an AI system – see MPEP § 2106.04(a)(2)(III))
If claim limitations, under the broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
In step 2A prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
iii. “a memory” (using a memory is considered a generic computer tool to apply an exception – see MPEP § 2106.05(f))
iv. “a processor circuit coupled to the memory, the processor circuit being configured to perform processing including” (a processor circuit is considered a generic computer tool to apply an exception – see MPEP § 2106.05(f))
v. “outputting a first set included in the first plurality of sets but not included in the second plurality of sets, as difference information, based on a result of the comparing” (insignificant extra-solution activity to apply the judicial exception – see MPEP § 2106.05(g))
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In step 2B of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional elements iii and iv recite generic computer components to apply an exception, and additional element v recites insignificant extra solution activity, which is well-understood, routine, conventional activity of presenting offers and gathering statistics, see OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93.
Consider the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 3, 4, 5, 6, and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Bhattacharyya et al. (US 11720845 B2) (hereinafter Bhattacharyya) in view of Zimmermann et al. (US 20220292331 A1) (hereinafter Zimmermann).
Claim 1:
a. Regarding claim 1, Bhattacharyya teaches “A non-transitory computer-readable recording medium… for causing a computer to execute processing"
See Bhattacharyya in col. 65, lines 28-37 where it describes “The drive unit includes a machine-readable medium on which is stored one or more sets of instructions (e.g., software) embodying any one or more of the methodologies or functions described herein. The software may also reside, completely or at least partially, within the main memory and/or within the processor during execution thereof by the computer, the main memory and the processor also constituting machine-readable media. The software may further be transmitted or received over a network via the network interface device.” Here, Bhattacharyya teaches a machine-readable recording medium. Further see Bhattacharyya in col. 64, lines 43-44 where it describes “The system may include a server computer”. Here, Bhattacharyya teaches processing executed on a computer.
b. Further, Bhattacharyya teaches “specifying a first plurality of sets of stakeholders that have relationships with each other, based on the configuration of the stakeholders of (a) system”
See Bhattacharyya in col. 52, lines 52-53 where it describes “The influence map 4130 can also indicate the relationship of stakeholders 4132 to each other.” Here, Bhattacharyya establishes an influence map of interrelated stakeholders. Further see Bhattacharyya in col. 53, lines 1-10 where it describes “in some embodiments, the influence map 4130 may also include one or more control points where a stakeholder interacts with at least one of: another stakeholder, a process, and a machine. In some embodiments, a user may provide a qualitative description of one or more control points. As used herein, modes may refer to a channel or touchpoint a stakeholder may interface with, such as people, process user interfaces, machines, culture, ambience and hearsay.” Here, Bhattacharyya teaches the influence map that has control points. These control points act as a configuration for the stakeholder or system in the form of a system of nodes that denote relationships between different stakeholders.
c. Further, Bhattacharyya teaches “comparing the first plurality of sets with a second plurality of sets of stakeholders determined based on the configuration of the stakeholders of another… system”
See Bhattacharyya in col 50, lines 11-14 where it describes “In some embodiments, the system 100 can rely on data gathered during other processes to identify additional stakeholders and processes impacted by the problem statement.” Here, Bhattacharyya teaches gathering a set of data which is to be compared to another set of data. Further, see Bhattacharyya in col. 53, lines 18-32 where it describes “The system 100 can update the influence map 4130 based on personnel data in the second database 207. Personnel data may relate to the one or more stakeholders and/or processes identified by the user in step 4001. The personnel data may correspond to data that was gathered in other processes (e.g., benchmarking, process decomposition, and KPIs). For example, personnel data gathered during the process decomposition may be used to associate stakeholders with a problem statement. In this way, the system 100 may be able to identify missing stakeholders and identify missing links between stakeholders. The updated influence map 4130 can be saved to the second database 207 as a new version, distinct from the initial influence map entered by the user in step 4001.” Here, Bhattacharyya teaches two pluralities of sets: the personnel data and the influence map, and identifies which stakeholders are included in one set but not the other.
d. Further, Bhattacharyya teaches “outputting a first set included in the first plurality of sets but not included in the second plurality of sets, as difference information, based on a result of the comparing”
See Bhattacharyya in col. 53, lines 18-32 where it describes “The system 100 can update the influence map 4130 based on personnel data in the second database 207. Personnel data may relate to the one or more stakeholders and/or processes identified by the user in step 4001. The personnel data may correspond to data that was gathered in other processes ( e.g., benchmarking, process decomposition, and KPIs). For example, personnel data gathered during the process decomposition may be used to associate stakeholders with a problem statement. In this way, the system 100 may be able to identify missing stakeholders and identify missing links between stakeholders. The updated influence map 4130 can be saved to the second database 207 as a new version, distinct from the initial influence map entered by the user in step 4001.” Here, Bhattacharyya identifies stakeholders which are missing from the influence map. Because they are included in the updated influence map but not the initial influence map, they are considered difference information. The updated influence map, including stakeholders from the one map but not the other, is then output to a database.
However, Bhattacharyya did not explicitly teach “storing an artificial intelligence (AI) system check program”, “stakeholders of an AI system”, or “another AI system”.
e. However, Zimmermann in the same field of art teaches “storing an artificial intelligence (AI) system check program.”
See Zimmermann in paragraph 0122 where it describes “As a possible application example, a personal AI system adapted to a user shall be considered. Ideally, such a system can develop intelligent behavior in the sense of hard artificial intelligence by coupling several artificial intelligence units that include, among other things, the described feedback by modulation as well as at least one evaluating unit with corresponding storage capabilities.” Here, Zimmermann describes an artificial intelligence system with an associated storage unit.
Further, Zimmermann teaches “stakeholders of an AI system;”
See Zimmermann in paragraph 0122 where it describes “In addition, user-specific behavior should be possible, so that the AI system can respond individually to a user, i.e. can in particular detect and/or learn which interests, idiosyncrasies, moods, emotions, character traits and which level of knowledge the user has.” Here, Zimmermann describes a user associated with the artificial intelligence, in essence a stakeholder for the system.
Further, Zimmermann teaches “another AI system;”
See Zimmermann in paragraph 0019 where it describes “In particular, a method is proposed in a system of at least a second and a third artificial intelligence unit, comprising: inputting first input values to said at least a second artificial intelligence unit, and obtaining output values based on said input values from said at least a second artificial intelligence unit; at least temporarily storing situation data, said situation data comprising at least one of first input values and second output values from said at least a second unit; and using the situation data as input values of the third artificial intelligence unit, the third artificial intelligence unit generating third output values in response to the input values; and checking whether the second output values of the at least one second unit satisfy one or more predetermined conditions based on the third output values. In this manner, one of the units may act as a validating unit that evaluates, monitors, or otherwise validates solutions from one or more other units in the system, particularly for compliance with certain constraints that may be predetermined or formed in the validating unit.” Here, Zimmermann describes a system with another artificial intelligence system.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the base reference of Bhattacharyya with the teachings of Zimmermann by using Bhattacharyya’s teachings of specifying sets of stakeholders, comparing them, and outputting data, and incorporate with Zimmermann’s teachings of incorporating an AI ethics compliance system and its stakeholders.
One of ordinary skill in the art would be motivated to do so because by integrating Zimmermann’s frameworks into the teachings of Bhattacharyya, one with ordinary skill in the art would “encourage satisfying and useful actions for the user and his environment; it can draw attention to moral-ethical problems adapted to the type, character situation and mood of the user and, for example, propagate certain virtues (helpfulness, generosity, kindness, courage, wisdom)”. (Zimmermann, paragraph 0131).
Claim 2
Regarding claim 2, Bhattacharyya in view of Zimmermann teaches the limitations in claim 1.
a. Further, Bhattacharyya teaches “outputting a second set included in the second plurality of sets but not included in the first plurality of sets, as the difference information, based on the result of the comparing.”
See Bhattacharyya in col. 53, lines 18-32 where it describes “The system 100 can update the influence map 4130 based on personnel data in the second database 207. Personnel data may relate to the one or more stakeholders and/or processes identified by the user in step 4001. The personnel data may correspond to data that was gathered in other processes ( e.g., benchmarking, process decomposition, and KPIs). For example, personnel data gathered during the process decomposition may be used to associate stakeholders with a problem statement. In this way, the system 100 may be able to identify missing stakeholders and identify missing links between stakeholders. The updated influence map 4130 can be saved to the second database 207 as a new version, distinct from the initial influence map entered by the user in step 4001.” Here, Bhattacharyya identifies stakeholders which are missing from the influence map. Because they are included in the updated influence map but not the initial influence map, they are considered difference information. The updated influence map, including stakeholders from the one map but not the other, is then output to a database.
Claim 3
Regarding claim 3, Bhattacharyya in view of Zimmermann teaches the limitations in claim 1.
a. Further, Bhattacharyya teaches “the first plurality of sets is determined based on the configuration of the stakeholders of the… system as a template;”
See Bhattacharyya in col. 52, lines 1-19 where it describes “As illustrated, the process 3703 includes identifying stakeholders. In some embodiments, the system 100 can present a user (e.g., the DT coordinator), via website 201, an influence map template corresponding to the adopted problem statement. The system 100 can identify stakeholders via an initial influence map completed by the user (step 4001). The initial influence map may be saved to the second database 207. An influence map may include a list of stakeholders of the target business that are affected by the adopted problem statement. As referred to herein, an impactee may be a stakeholder who is affected by the problem statement and an impactor may refer to a stakeholder that affects the problem statement. In some embodiments, the influence map template may allow the user to identify stakeholders, identify relationships between the stakeholders, identify the amount of control a stakeholder has over the processes related to the adopted problem statement, and identify touchpoints.” Here, Bhattacharyya establishes that the system has stakeholders. It identifies a set of stakeholders based on an influence map completed by the user, which is considered a template.
b. Further, Bhattacharyya teaches “the second plurality of sets is determined based on the configuration of the another… system as a project;”
See Bhattacharyya in col. 53, lines 18-32 where it describes “The system 100 can update the influence map 4130 based on personnel data in the second database 207. Personnel data may relate to the one or more stakeholders and/or processes identified by the user in step 4001. The personnel data may correspond to data that was gathered in other processes (e.g., benchmarking, process decomposition, and KPIs). For example, personnel data gathered during the process decomposition may be used to associate stakeholders with a problem statement. In this way, the system 100 may be able to identify missing stakeholders and identify missing links between stakeholders. The updated influence map 4130 can be saved to the second database 207 as a new version, distinct from the initial influence map entered by the user in step 4001.” Here, Bhattacharyya teaches determining a set of stakeholders, using the personnel data and updated influence map as a project.
However, Bhattacharyya did not explicitly teach “an AI system” or “another AI system”.
c. However, Zimmermann in the same field of art teaches “an AI system.”
See Zimmermann in paragraph 0122 where it describes “As a possible application example, a personal AI system adapted to a user shall be considered. Ideally, such a system can develop intelligent behavior in the sense of hard artificial intelligence by coupling several artificial intelligence units that include, among other things, the described feedback by modulation as well as at least one evaluating unit with corresponding storage capabilities.” Here, Zimmermann teaches an artificial intelligence system and its associated storage unit.
Further, Zimmermann teaches “another AI system;”
See Zimmermann in paragraph 0019 where it describes “In particular, a method is proposed in a system of at least a second and a third artificial intelligence unit, comprising: inputting first input values to said at least a second artificial intelligence unit, and obtaining output values based on said input values from said at least a second artificial intelligence unit; at least temporarily storing situation data, said situation data comprising at least one of first input values and second output values from said at least a second unit; and using the situation data as input values of the third artificial intelligence unit, the third artificial intelligence unit generating third output values in response to the input values; and checking whether the second output values of the at least one second unit satisfy one or more predetermined conditions based on the third output values. In this manner, one of the units may act as a validating unit that evaluates, monitors, or otherwise validates solutions from one or more other units in the system, particularly for compliance with certain constraints that may be predetermined or formed in the validating unit.” Here, Zimmermann describes a system with another artificial intelligence system.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the base reference of Bhattacharyya with the teachings of Zimmermann by using Bhattacharyya’s teachings of determining sets of stakeholders, and incorporate with Zimmermann’s teachings of incorporating an AI ethics compliance system.
One of ordinary skill in the art would be motivated to do so because by integrating Zimmermann’s frameworks into the teachings of Bhattacharyya, one with ordinary skill in the art would “encourage satisfying and useful actions for the user and his environment; it can draw attention to moral-ethical problems adapted to the type, character situation and mood of the user and, for example, propagate certain virtues (helpfulness, generosity, kindness, courage, wisdom)”. (Zimmermann, paragraph 0131).
Claim 4
Regarding claim 4, Bhattacharyya in view of Zimmermann teaches the limitations in claim 1.
a. Further, Bhattacharyya teaches “choosing one set of the first plurality of sets from among two or more sets of the first plurality of sets, based on similarity between a document about the two or more sets of the first plurality of sets included in the… system and the document included in the another… system;”
See Bhattacharyya in col. 53, lines 12-15 where it describes “Based on the visual and qualitative descriptions of control in the influence map 4130, a user may be able to identify which stakeholders exert the most control and intensity of this control.” Here, Bhattacharyya describes choosing one of several sets of stakeholders. Further see Bhattacharyya in col. 53, lines 20-24 where it describes “Personnel data may relate to the one or more stakeholders and/or processes identified by the user in step 4001. The personnel data may correspond to data that was gathered in other processes (e.g., benchmarking, process decomposition, and KPIs).” Here, Bhattacharyya describes personnel data that is derived from one or more of the sets of stakeholders. Because it is stated that the personnel data may relate to the stakeholders or processes identified by the user, or through other processes, it follows that the sets may be chosen based on user-defined parameters such as a document.
However, Bhattacharyya did not explicitly teach “an AI system” or “another AI system”.
c. However, Zimmermann in the same field of art teaches “an AI system.”
See Zimmermann in paragraph 0122 where it describes “As a possible application example, a personal AI system adapted to a user shall be considered. Ideally, such a system can develop intelligent behavior in the sense of hard artificial intelligence by coupling several artificial intelligence units that include, among other things, the described feedback by modulation as well as at least one evaluating unit with corresponding storage capabilities.” Here, Zimmermann teaches an artificial intelligence system and its associated storage unit.
Further, Zimmermann teaches “another AI system;”
See Zimmermann in paragraph 0019 where it describes “In particular, a method is proposed in a system of at least a second and a third artificial intelligence unit, comprising: inputting first input values to said at least a second artificial intelligence unit, and obtaining output values based on said input values from said at least a second artificial intelligence unit; at least temporarily storing situation data, said situation data comprising at least one of first input values and second output values from said at least a second unit; and using the situation data as input values of the third artificial intelligence unit, the third artificial intelligence unit generating third output values in response to the input values; and checking whether the second output values of the at least one second unit satisfy one or more predetermined conditions based on the third output values. In this manner, one of the units may act as a validating unit that evaluates, monitors, or otherwise validates solutions from one or more other units in the system, particularly for compliance with certain constraints that may be predetermined or formed in the validating unit.” Here, Zimmermann describes a system with another artificial intelligence system.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the base reference of Bhattacharyya with the teachings of Zimmermann by using Bhattacharyya’s teachings of defining two pluralities of sets, and incorporate with Zimmermann’s teachings of incorporating an AI ethics compliance system.
One of ordinary skill in the art would be motivated to do so because by integrating Zimmermann’s frameworks into the teachings of Bhattacharyya, one with ordinary skill in the art would “encourage satisfying and useful actions for the user and his environment; it can draw attention to moral-ethical problems adapted to the type, character situation and mood of the user and, for example, propagate certain virtues (helpfulness, generosity, kindness, courage, wisdom)”. (Zimmermann, paragraph 0131).
Claim 5
Regarding claim 4, Bhattacharyya in view of Zimmermann teaches the limitations in claim 1.
a. Further, Bhattacharyya teaches “reflecting the difference information in the another… system”
See Bhattacharyya in col. 53, lines 28-30 where it describes “The system 100 may be able to identify missing stakeholders and identify missing links between stakeholders.” Here, Bhattacharyya teaches identifying missing links, representing the difference information between two sets of stakeholders.
However, Bhattacharyya did not explicitly teach “another AI system.”
b. However, Zimmermann in the same field of art teaches “another AI system.”
See Zimmermann in paragraph 0019 where it describes “In particular, a method is proposed in a system of at least a second and a third artificial intelligence unit, comprising: inputting first input values to said at least a second artificial intelligence unit, and obtaining output values based on said input values from said at least a second artificial intelligence unit; at least temporarily storing situation data, said situation data comprising at least one of first input values and second output values from said at least a second unit; and using the situation data as input values of the third artificial intelligence unit, the third artificial intelligence unit generating third output values in response to the input values; and checking whether the second output values of the at least one second unit satisfy one or more predetermined conditions based on the third output values. In this manner, one of the units may act as a validating unit that evaluates, monitors, or otherwise validates solutions from one or more other units in the system, particularly for compliance with certain constraints that may be predetermined or formed in the validating unit.” Here, Zimmermann describes a system with another artificial intelligence system.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the base reference of Bhattacharyya with the teachings of Zimmermann by using Bhattacharyya’s teachings of reflecting the difference information in a system, and incorporate with Zimmermann’s teachings of incorporating an AI ethics compliance system.
One of ordinary skill in the art would be motivated to do so because by integrating Zimmermann’s frameworks into the teachings of Bhattacharyya, one with ordinary skill in the art would “encourage satisfying and useful actions for the user and his environment; it can draw attention to moral-ethical problems adapted to the type, character situation and mood of the user and, for example, propagate certain virtues (helpfulness, generosity, kindness, courage, wisdom)”. (Zimmermann, paragraph 0131).
Claim 6
Regarding claim 6, Bhattacharyya teaches “(a) system check method implemented by a computer,”
See Bhattacharyya in col. 4, lines 10-22 where it describes “Embodiments of this disclosure may relate to a system for improving multiple areas, such as strategy, operations, risk management, and regulation compliance, of a target business. FIG. 1 illustrates a block diagram of a system 100, according to embodiments of this disclosure. The system 100 may provide different functionalities to achieve these improvements. For example, the system 100 may include functionality to provide one or more initial scores of a target business, assess one or more process capabilities of the target business, set one or more KPIs, set one or more business goals, provide a collaboration platform for stakeholders in the target business, and provide a roadmap for achieving one or more business goals.” Here, Bhattacharyya describes a system that can check for various functionalities. Further see Bhattacharyya in col. 64, lines 43-44 where it describes “The system may include a server computer”. Here, Bhattacharyya teaches processing executed on a computer.
a. Further, Bhattacharyya teaches “specifying a first plurality of sets of stakeholders that have relationships with each other, based on the configuration of the stakeholders of (a) system”
See Bhattacharyya in col. 52, lines 52-53 where it describes “The influence map 4130 can also indicate the relationship of stakeholders 4132 to each other.” Here, Bhattacharyya establishes an influence map of interrelated stakeholders. Further see Bhattacharyya in col. 53, lines 1-10 where it describes “in some embodiments, the influence map 4130 may also include one or more control points where a stakeholder interacts with at least one of: another stakeholder, a process, and a machine. In some embodiments, a user may provide a qualitative description of one or more control points. As used herein, modes may refer to a channel or touchpoint a stakeholder may interface with, such as people, process user interfaces, machines, culture, ambience and hearsay.” Here, Bhattacharyya teaches the influence map that has control points. These control points act as a configuration for the stakeholder or system in the form of a system of nodes that denote relationships between different stakeholders.
b. Further, Bhattacharyya teaches “comparing the first plurality of sets with a second plurality of sets of stakeholders determined based on the configuration of the stakeholders of another… system”
See Bhattacharyya in col 50, lines 11-14 where it describes “In some embodiments, the system 100 can rely on data gathered during other processes to identify additional stakeholders and processes impacted by the problem statement.” Here, Bhattacharyya teaches gathering a set of data which is to be compared to another set of data. Further, see Bhattacharyya in col. 53, lines 18-32 where it describes “The system 100 can update the influence map 4130 based on personnel data in the second database 207. Personnel data may relate to the one or more stakeholders and/or processes identified by the user in step 4001. The personnel data may correspond to data that was gathered in other processes (e.g., benchmarking, process decomposition, and KPIs). For example , personnel data gathered during the process decomposition may be used to associate stakeholders with a problem statement. In this way, the system 100 may be able to identify missing stakeholders and identify missing links between stakeholders. The updated influence map 4130 can be saved to the second database 207 as a new version, distinct from the initial influence map entered by the user in step 4001.” Here, Bhattacharyya teaches two pluralities of sets: the personnel data and the influence map.
c. Further, Bhattacharyya teaches “outputting a first set included in the first plurality of sets but not included in the second plurality of sets, as difference information, based on a result of the comparing”
See Bhattacharyya in col. 53, lines 18-32 where it teaches “The system 100 can update the influence map 4130 based on personnel data in the second database 207. Personnel data may relate to the one or more stakeholders and/or processes identified by the user in step 4001. The personnel data may correspond to data that was gathered in other processes ( e.g., benchmarking, process decomposition, and KPIs). For example, personnel data gathered during the process decomposition may be used to associate stakeholders with a problem statement. In this way, the system 100 may be able to identify missing stakeholders and identify missing links between stakeholders. The updated influence map 4130 can be saved to the second database 207 as a new version, distinct from the initial influence map entered by the user in step 4001.” Here, Bhattacharyya identifies stakeholders which are missing from the influence map. Because they are included in the updated influence map but not the initial influence map, they are considered difference information. The updated influence map, including stakeholders from the one map but not the other, is then output to a database.
However, Bhattacharyya did not explicitly teach “an artificial intelligence (AI) system check method”, “stakeholders of an AI system”, or “another AI system”.
However, Zimmermann in the same field of art teaches “storing an artificial intelligence (AI) system check program.”
See Zimmermann in paragraph 0122 where it describes “As a possible application example, a personal AI system adapted to a user shall be considered. Ideally, such a system can develop intelligent behavior in the sense of hard artificial intelligence by coupling several artificial intelligence units that include, among other things, the described feedback by modulation as well as at least one evaluating unit with corresponding storage capabilities.” Here, Zimmermann describes an artificial intelligence system with an associated storage unit.
Further, Zimmermann teaches “stakeholders of an AI system;”
See Zimmermann in paragraph 0122 where it describes “In addition, user-specific behavior should be possible, so that the AI system can respond individually to a user, i.e. can in particular detect and/or learn which interests, idiosyncrasies, moods, emotions, character traits and which level of knowledge the user has.” Here, Zimmermann describes a user associated with the artificial intelligence, in essence a stakeholder for the system.
Further, Zimmermann teaches “another AI system;”
See Zimmermann in paragraph 0019 where it describes “In particular, a method is proposed in a system of at least a second and a third artificial intelligence unit, comprising: inputting first input values to said at least a second artificial intelligence unit, and obtaining output values based on said input values from said at least a second artificial intelligence unit; at least temporarily storing situation data, said situation data comprising at least one of first input values and second output values from said at least a second unit; and using the situation data as input values of the third artificial intelligence unit, the third artificial intelligence unit generating third output values in response to the input values; and checking whether the second output values of the at least one second unit satisfy one or more predetermined conditions based on the third output values. In this manner, one of the units may act as a validating unit that evaluates, monitors, or otherwise validates solutions from one or more other units in the system, particularly for compliance with certain constraints that may be predetermined or formed in the validating unit.” Here, Zimmermann describes a system with another artificial intelligence system.
One of ordinary skill in the art would be motivated to do so because by integrating Zimmermann’s frameworks into the teachings of Bhattacharyya, one with ordinary skill in the art would “encourage satisfying and useful actions for the user and his environment; it can draw attention to moral-ethical problems adapted to the type, character situation and mood of the user and, for example, propagate certain virtues (helpfulness, generosity, kindness, courage, wisdom)”. (Zimmermann, paragraph 0131).
Claim 7
Regarding claim 7, Bhattacharyya teaches “an information processing apparatus, comprising:”
See Bhattacharyya in col. 64, lines 61-65 where it describes “The server computer may be a machine such as a computer, within which a set of instructions, causes the machine to perform any of the methodologies discussed herein, may be executed, according to embodiments of the disclosure.” Here, Bhattacharya describes a machine that processes information, such as a computer.
a. Further, Bhattacharyya teaches “a memory,”
See Bhattacharyya in col. 65, lines 17-20 where it describes “dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), and a static memory (e.g., flash memory, static random access memory (SRAM), etc.), which can communicate with each other via a bus.” Here, Bhattacharyya describes a memory included as part of a computer.
b. Further, Bhattacharyya teaches “a processor circuit coupled to the memory,”
See Bhattacharyya in col. 65, lines 31-37 where it describes “The software may also reside, completely or at least partially, within the main memory and/or within the processor during execution thereof by the computer, the main memory and the processor also constituting machine-readable media. The software may further be transmitted or received over a network via the network interface device.” Here, Bhattacharyya describes a processor that may reside within the memory.
c. Further, Bhattacharyya teaches “the processor circuit being configured to perform processing,”
See Bhattacharyya in col. 65, lines 28-31 where it describes “The drive unit includes a machine-readable medium on which is stored one or more sets of instructions (e.g., software) embodying any one or more of the methodologies or functions described herein.” Here, Bhattacharyya describes a unit in the processor that is configured to perform instructions.
d. Further, Bhattacharyya teaches “specifying a first plurality of sets of stakeholders that have relationships with each other, based on the configuration of the stakeholders of (a) system”
See Bhattacharyya in col. 52, lines 52-53 where it describes “The influence map 4130 can also indicate the relationship of stakeholders 4132 to each other.” Here, Bhattacharyya establishes an influence map of interrelated stakeholders. Further see Bhattacharyya in col. 53, lines 1-10 where it describes “in some embodiments, the influence map 4130 may also include one or more control points where a stakeholder interacts with at least one of: another stakeholder, a process, and a machine. In some embodiments, a user may provide a qualitative description of one or more control points. As used herein, modes may refer to a channel or touchpoint a stakeholder may interface with, such as people, process user interfaces, machines, culture, ambience and hearsay.” Here, Bhattacharyya teaches the influence map that has control points. These control points act as a configuration for the stakeholder or system in the form of a system of nodes that denote relationships between different stakeholders.
e. Further, Bhattacharyya teaches “comparing the first plurality of sets with a second plurality of sets of stakeholders determined based on the configuration of the stakeholders of another… system”
See Bhattacharyya in col 50, lines 11-14 where it describes “In some embodiments, the system 100 can rely on data gathered during other processes to identify additional stakeholders and processes impacted by the problem statement.” Here, Bhattacharyya teaches gathering a set of data which is to be compared to another set of data. Further, see Bhattacharyya in col. 53, lines 18-32 where it describes “The system 100 can update the influence map 4130 based on personnel data in the second database 207. Personnel data may relate to the one or more stakeholders and/or processes identified by the user in step 4001. The personnel data may correspond to data that was gathered in other processes (e.g., benchmarking, process decomposition, and KPIs). For example , personnel data gathered during the process decomposition may be used to associate stakeholders with a problem statement. In this way, the system 100 may be able to identify missing stakeholders and identify missing links between stakeholders. The updated influence map 4130 can be saved to the second database 207 as a new version, distinct from the initial influence map entered by the user in step 4001.” Here, Bhattacharyya teaches two pluralities of sets: the personnel data and the influence map.
f. Further, Bhattacharyya teaches “outputting a first set included in the first plurality of sets but not included in the second plurality of sets, as difference information, based on a result of the comparing”
See Bhattacharyya in col. 53, lines 18-32 where it teaches “The system 100 can update the influence map 4130 based on personnel data in the second database 207. Personnel data may relate to the one or more stakeholders and/or processes identified by the user in step 4001. The personnel data may correspond to data that was gathered in other processes ( e.g., benchmarking, process decomposition, and KPIs). For example, personnel data gathered during the process decomposition may be used to associate stakeholders with a problem statement. In this way, the system 100 may be able to identify missing stakeholders and identify missing links between stakeholders. The updated influence map 4130 can be saved to the second database 207 as a new version, distinct from the initial influence map entered by the user in step 4001.” Here, Bhattacharyya identifies stakeholders which are missing from the influence map. Because they are included in the updated influence map but not the initial influence map, they are considered difference information. The updated influence map, including stakeholders from the one map but not the other, is then output to a database.
However, Bhattacharyya did not explicitly teach “an artificial intelligence (AI) system” or “another AI system”.
However, Zimmermann in the same field of art teaches “storing an artificial intelligence (AI) system check program.”
See Zimmermann in paragraph 0122 where it describes “As a possible application example, a personal AI system adapted to a user shall be considered. Ideally, such a system can develop intelligent behavior in the sense of hard artificial intelligence by coupling several artificial intelligence units that include, among other things, the described feedback by modulation as well as at least one evaluating unit with corresponding storage capabilities.” Here, Zimmermann describes an artificial intelligence system with an associated storage unit.
Further, Zimmermann teaches “another AI system;”
See Zimmermann in paragraph 0019 where it describes “In particular, a method is proposed in a system of at least a second and a third artificial intelligence unit, comprising: inputting first input values to said at least a second artificial intelligence unit, and obtaining output values based on said input values from said at least a second artificial intelligence unit; at least temporarily storing situation data, said situation data comprising at least one of first input values and second output values from said at least a second unit; and using the situation data as input values of the third artificial intelligence unit, the third artificial intelligence unit generating third output values in response to the input values; and checking whether the second output values of the at least one second unit satisfy one or more predetermined conditions based on the third output values. In this manner, one of the units may act as a validating unit that evaluates, monitors, or otherwise validates solutions from one or more other units in the system, particularly for compliance with certain constraints that may be predetermined or formed in the validating unit.” Here, Zimmermann describes a system with another artificial intelligence system.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the base reference of Bhattacharyya with the teachings of Zimmermann by using Bhattacharyya’s teachings of specifying sets of stakeholders, comparing them, and outputting data, and incorporate with Zimmermann’s teachings of incorporating an AI ethics compliance system.
One of ordinary skill in the art would be motivated to do so because by integrating Zimmermann’s frameworks into the teachings of Bhattacharyya, one with ordinary skill in the art would “encourage satisfying and useful actions for the user and his environment; it can draw attention to moral-ethical problems adapted to the type, character situation and mood of the user and, for example, propagate certain virtues (helpfulness, generosity, kindness, courage, wisdom)”. (Zimmermann, paragraph 0131).
Conclusion
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/ANDREW CHARLES YORKS/Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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