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 JP2022-149505, filed on September 20, 2022, has been electronically retrieved by USPTO.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on July 3, 2023 and November 2, 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-9 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
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 storing a rule formation support program causing a computer to execute a process”, and a computer-readable recording medium or machine 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:
“specifying an attribute that includes a difference equal to or greater than a predetermined value in the selection frequency of each attribute by comparing the first tendency and the second tendency” (this is a mental process, a person could mentally evaluate calculating a difference for an attribute between two tendencies and determining if it is equal to or greater than a predetermined value and specifying it, 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:
“obtaining, based on history information of transactions by each user, a first tendency that indicates a selection frequency of each attribute in the transactions by a plurality of users and a second tendency that indicates a selection frequency of each attribute in the transactions by a predetermined target user;” (Obtaining tendencies based on history information is considered insignificant extra-solution activity of mere data gathering – see MPEP § 2106.05(g)),
“when generating data to be used for one of creating and updating a model that corresponds to a rule of an attribute presented to the target user by integrating the first tendency and the second tendency, performing integration by using the second tendency for the specified attribute.” (Generating data to perform integration is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f))
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional 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 ii recites insignificant extra-solution activity of mere data gathering, which is a well understood routine and conventional activity, receiving or transmitting data over a network, e.g., using the Internet to gather data, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362. In addition, additional element iii recites mere instructions to apply the judicial exception using generic computer components, which are not indicative of significantly more.
Considering 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:
“The non-transitory computer-readable recording medium according to claim 1, wherein, in the specifying, a value obtained by normalizing the selection frequency of each attribute in the first tendency is compared with a value obtained by normalizing the selection frequency of each attribute in the second tendency, and the attribute that includes a difference equal to or greater than a predetermined value between the normalized values is specified.” (this is a mental process, a person could mentally evaluate normalizing a frequency for a value for specification, 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 compute 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 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:
“The non-transitory computer-readable recording medium according to claim 1, wherein the history information indicates a history of attributes assigned to document information forwarded between users, and wherein, in the performing integration, data to be used for one of creating and updating a model that corresponds to a rule for presenting an attribute assigned when the target user forwards the document information is generated.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 4:
Regarding claim 4, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A rule formation support method causing a computer to execute a process”, and a rule formation support method or process 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:
“specifying an attribute that includes a difference equal to or greater than a predetermined value in the selection frequency of each attribute by comparing the first tendency and the second tendency” (this is a mental process, a person could mentally evaluate calculating a difference for an attribute between two tendencies and determining if it is equal to or greater than a predetermined value and specifying it, 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-analysios set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“obtaining, based on history information of transactions by each user, a first tendency that indicates a selection frequency of each attribute in the transactions by a plurality of users and a second tendency that indicates a selection frequency of each attribute in the transactions by a predetermined target user;” (Obtaining tendencies based on history information is considered insignificant extra-solution activity of mere data gathering – see MPEP § 2106.05(g)),
“when generating data to be used for one of creating and updating a model that corresponds to a rule of an attribute presented to the target user by integrating the first tendency and the second tendency, performing integration by using the second tendency for the specified attribute.” (Generating data to perform integration is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f))
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional 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 ii recites insignificant extra-solution activity of mere data gathering, which is a well understood routine and conventional activity, receiving or transmitting data over a network, e.g., using the Internet to gather data, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362. In addition, additional element iii recites mere instructions to apply the judicial exception using generic computer components, which are not indicative of significantly more.
Considering 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 5:
Regarding claim 5, it is dependent upon claim 4, and thereby incorporates the limitations of, and corresponding analysis to claim 4. Further, claim 5 recites the following additional elements:
“The rule formation support method according to claim 4, wherein, in the specifying, a value obtained by normalizing the selection frequency of each attribute in the first tendency is compared with a value obtained by normalizing the selection frequency of each attribute in the second tendency, and the attribute that includes a difference equal to or greater than a predetermined value between the normalized values is specified.” (this is a mental process, a person could mentally evaluate normalizing a frequency for a value for specification, 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 compute 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 6:
Regarding claim 6, it is dependent upon claim 4, and thereby incorporates the limitations of, and corresponding analysis to claim 4. Further, claim 6 recites the following additional elements:
“The rule formation support method according to claim 4, wherein the history information indicates a history of attributes assigned to document information forwarded between users, and wherein, in the performing integration, data to be used for one of creating and updating a model that corresponds to a rule for presenting an attribute assigned when the target user forwards the document information is generated.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 7:
Regarding claim 7, in step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A rule formation support apparatus comprising: a memory; and a processor couple to the memory”, and a rule formation support apparatus or machine 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:
“specify an attribute that includes a difference equal to or greater than a predetermined value in the selection frequency of each attribute by comparing the first tendency and the second tendency” (this is a mental process, a person could mentally evaluate calculating a difference for an attribute between two tendencies and determining if it is equal to or greater than a predetermined value and specifying it, 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-analysios set forth in MPEP 2106, the examiner has determined that the following additional elements do not integrate this judicial exception into a practical application:
“A rule formation support apparatus comprising: a memory; and a processor couple to the memory” (Using memory and processor is considered generic computer tool to apply an exception – see MPEP § 2106.05(f)),
“obtain, based on history information of transactions by each user, a first tendency that indicates a selection frequency of each attribute in the transactions by a plurality of users and a second tendency that indicates a selection frequency of each attribute in the transactions by a predetermined target user” (Obtaining tendencies based on history information is considered insignificant extra-solution activity of mere data gathering – see MPEP § 2106.05(g)),
“when generating data to be used for one of creating and updating a model that corresponds to a rule of an attribute presented to the target user by integrating the first tendency and the second tendency, perform integration by using the second tendency for the specified attribute.” (Generating data to perform integration is considered mere instructions to apply an exception using generic computer – see MPEP § 2106.05(f))
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional 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 ii recites generic computer tools to apply an exception, additional element iii recites insignificant extra-solution activity of mere data gathering, which is a well understood routine and conventional activity, receiving or transmitting data over a network, e.g., using the Internet to gather data, see Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362. In addition, additional element iv recites mere instructions to apply the judicial exception using generic computer components, which are not indicative of significantly more.
Considering 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 8:
Regarding claim 8, it is dependent upon claim 7, and thereby incorporates the limitations of, and corresponding analysis to claim 7. Further, claim 8 recites the following additional elements:
“The rule formation support apparatus according to claim 7, wherein the processor is configured to compare a value obtained by normalizing the selection frequency of each attribute in the first tendency with a value obtained by normalizing the selection frequency of each attribute in the second tendency, and specify the attribute that includes a difference equal to or greater than a predetermined value between the normalized values.” this is a mental process, a person could mentally evaluate comparing a value and normalizing a frequency for a value for specification, 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 compute components, then it falls within the mental process grouping of abstract ideas. Accordingly, the claim “recites” an abstract idea.
“The rule formation support apparatus according to claim 7, wherein the processor is configured to…” (this is considered using generic computer component to apply an exception, 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 9:
Regarding claim 9, it is dependent upon claim 7, and thereby incorporates the limitations of, and corresponding analysis to claim 7. Further, claim 9 recites the following additional elements:
“The rule formation support apparatus according to claim 7, wherein the history information indicates a history of attributes assigned to document information forwarded between users, and wherein the processor is configured to generate data to be used for one of creating and updating a model that corresponds to a rule for presenting an attribute assigned when the target user forwards the document information.” (In step 2A, prong 2, this is considered mere instructions to apply an exception using generic computer, see MPEP § 2106.05(f)). (In step 2B, this is also considered mere instructions to apply an exception using 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 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.
Claim(s) 1, 3, 4 ,6, 7, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Khare V. et al, (US. Patent 10,073,892 B1) filed on June 12, 2015, (hereafter Khare), in view of D'Alessandro A. et al, (US. Patent Application Publication 20230134392 A1) filed on November 2, 2021, (hereafter D'Alessandro).
Claim 1:
Regarding claim 1, Khare teaches “obtaining, based on history information of transactions by each user, a first tendency that indicates a selection frequency of each attribute in the transactions by a plurality of users and a second tendency that indicates a selection frequency of each attribute in the transactions by a predetermined target user;”
See Khare in column 2 lines 22-39 where it describes “In accordance with an illustrative embodiment, a computer-implemented recommendation service determines a number of frequent attribute-value tuples based on historical item acquisition data (e.g., purchase histories or other item acquisition histories). An item may be considered acquired by a user when the item is purchased, rented, licensed, downloaded, installed, added to a wish list, saved, tagged, recommended, or subscribed to by the user. Illustratively, the recommendation service may employ various frequent itemset mining methods to identify meaningful frequent attribute-value tuples. As described above, the mining methods can be applied to transactions represented by attribute-values rather than items. Thresholds or other criteria can be used to configure the frequent attribute-value tuple mining process. For example, any frequent attribute-value tuple must correspond to a minimum number of distinct transactions or a minimum number of distinct users associated with the transactions.” Here, Khare establishes obtaining a history of information from users which are in relation to transactions. Khare also establishes the first tendency with the frequent attribute tuple which is based on transactions made by a distinct number of users which is a plurality Further, see Khare in column 2 lines 40-54 describing “The recommendation service may associate user interest measures with individual frequent attribute-value tuples. For example, a user's explicit rating of an item can be converted to the user's interest measure for a frequent attribute-value tuple that corresponds to the item. Alternatively or in addition, a user interest measure for a particular frequent attribute-value tuple can be derived from the user's interactions (e.g., past purchases) with items corresponding to the particular frequent attribute-value tuple.” Here, Khare establishes a singular user’s interest measure in relation to selection frequency. Further see Khare in column 7 lines 23-31 describing “Illustratively, the recommendation service 150 may examine user interest measures that link the target user to frequent attribute-value tuples as generated by the routine of FIG. 3 (e.g., by parsing a row of data corresponding to the target user in one or more matrices of user interest measures as maintained by the frequent attribute database 173), and may sort or order frequent attribute-value tuples into a list based on their corresponding user interest measures with respect to the target user.” Here this can be seen as the second tendency in relation to the predetermined target user as it uses a singular user’s interest measures.
Further, Khare teaches, “specifying an attribute that includes a difference equal to or greater than a predetermined value in the selection frequency of each attribute by comparing the first tendency and the second tendency;”
See Khare in column 2 lines 55-63 where it describes “The recommendation service may then generate recommendations for any individual user based on the frequent attribute-value tuples and the user interest measures. For example, the recommendation service may identify one or more frequent attribute-value tuples associated with sufficiently high user interest measures (e.g., above a predetermined threshold) with respect to a particular user, and use these frequent attribute-value tuples as a basis for recommending items to the particular user.” Here Khare teaches specifying an attribute with the generation of a recommendation which is based on the frequent attribute-value tuple and the user interest measures. In the previous limitation we established that the tuples could be seen as a first tendency and the user interest measures are associated with the second tendency. The difference equal to or greater than a predetermined value is the same as being above a predetermined threshold which is obtained by the comparison of the tendencies which is taught here.
However, Khare did not explicitly teach “A non-transitory computer-readable recording medium storing a rule formation support program causing a computer to execute a process, and when generating data to be used for one of creating and updating a model that corresponds to a rule of an attribute presented to the target user by integrating the first tendency and the second tendency, performing integration by using the second tendency for the specified attribute.”
Further, D’Alessandro in the same field of art teaches, “A non-transitory computer-readable recording medium storing a rule formation support program causing a computer to execute a process, and when generating data to be used for one of creating and updating a model that corresponds to a rule of an attribute presented to the target user by integrating the first tendency and the second tendency, performing integration by using the second tendency for the specified attribute.”
See D’Alessandro in paragraph [0006] where it describes “a non-transitory computer readable storage medium having embodied thereon a program is provided. The program is executable by a processor to perform a method of automated account interaction.” Here, D’Alessandro establishes the non-transitory computer readable storage medium to execute a process. Further see D’Alessandro in paragraph [0030] where it describes “The system uses the trained ML models to generate a recommended transaction at least in part by inputting the intent for the transaction to the trained ML models. In some examples, the training data for the one or more ML models can include historical information, an intent for the transaction identified in the historical information, and/or a second transaction performed by the user after the transaction identified in the historical information. The second transaction may be related to the intent and/or to the transaction identified in the historical information. During a validation stage of training, the system can use the one or more ML models to generate a recommended transaction based on the intent and/or based on the historical information, and the system can update and/or further train the one or more ML models based on whether or not the recommended transaction matches the second transaction from the training data.” Here D’Alessandro establishes data used to update a ML model based on a recommended transaction and second transaction which can be seen as integration of a first tendency and second tendency in an analogous system and the recommended transaction can be treated as the integrated second tendency due to it being the specified transaction. In this, the recommended transaction can be seen as the rule of an attribute presented to the target user, the recommended transaction includes an identified intent for the transaction and this identified intent dome by the system is being interpreted as the technique or rule of an attribute.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Khare with the teachings of D’Alessandro by using Khare’s teachings of obtaining tendencies and specifying an attribute, and incorporate with D’Alessandro’s teaching of performing integration.
One of ordinary skill in the art would be motivated to do so because by integrating D’Alessandro’s frameworks into the methods of Khare, one with ordinary skill in the art would “provide customized recommendations (e.g., recommended transactions) that are customized and/or tailored specifically to users based on their histories, their account information, intents determined behind their transaction(s), or combinations thereof. This improves over systems that are unable to provide recommendations, or provide standardized recommendations without such customization.” (D’Alessandro, paragraph [0034]).
Claim 3:
Regarding claim 3, Khare in view of D’Alessandro teaches the limitations in claim 1.
Further D’Alessandro teaches, “The non-transitory computer-readable recording medium according to claim 1, wherein the history information indicates a history of attributes assigned to document information forwarded between users, and wherein, in the performing integration, data to be used for one of creating and updating a model that corresponds to a rule for presenting an attribute assigned when the target user forwards the document information is generated.”
See D’Alessandro in paragraph [0003] where it describes “The historical information can include, for example, demographic data, transaction histories, credit histories, account histories of the account, characteristics of the user, actions performed by the user and/or using the user account, and the like.” Here, D’Alessandro establishes the historical information which can consist of document information indicating a history of different attributes. Further see D’Alessandro again in paragraph [0003] where it describes “The system uses the one or more trained machine learning models to identify an intent for the transaction at least in part by inputting the historical information to the one or more trained machine learning models. The system provides the intent for the transaction to the one or more trained machine learning models. The system uses the trained machine learning models to generate a recommended transaction at least in part by inputting the intent for the transaction to the trained machine learning models.” Here D’Alessandro establishes document information being forwarded by inputting the historical information to the ML model which updates a model corresponding to a target user forwarding document information, data is then generated by the recommended transaction. As mentioned in previous limitations, the recommended transaction includes an identified intent for the transaction and this identified intent dome by the system is being interpreted as the technique or rule of an attribute. Further see D’Alessandro in paragraph [0036] where it describes “In some cases, user device 120 may receive an input from a user of the user device 120.” Here D’Alessandro establishes a user device being able to receive an input from a user. Further see D’Alessandro in paragraph [0054] where it describes “In some examples, an agent device 260 may communicate with a user device 120, either directly or through the user front-end 202, the agent back-end 210, the automation engine 216, the web server(s) 208, or a combination thereof. For example, an agent using the agent device 260 may communicate with a user using the user device 120. The agent may be a human operator or an artificial intelligence (AI) assistant.” Here D’Alessandro establishes a communication between two users which can be linked two document information being forwarded between a user and an agent or other user and the user can input information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Khare with the teachings of D’Alessandro by using Khare’s teachings of obtaining tendencies and specifying an attribute, and incorporate with D’Alessandro’s teaching of performing integration.
One of ordinary skill in the art would be motivated to do so because by integrating D’Alessandro’s frameworks into the methods of Khare, one with ordinary skill in the art would “provide customized recommendations (e.g., recommended transactions) that are customized and/or tailored specifically to users based on their histories, their account information, intents determined behind their transaction(s), or combinations thereof. This improves over systems that are unable to provide recommendations, or provide standardized recommendations without such customization.” (D’Alessandro, paragraph [0034]).
Claim 4:
Regarding claim 4, Khare teaches “A rule formation support method causing a computer to execute a process, the process comprising: obtaining, based on history information of transactions by each user, a first tendency that indicates a selection frequency of each attribute in the transactions by a plurality of users and a second tendency that indicates a selection frequency of each attribute in the transactions by a predetermined target user;”
See Khare in column 1 lines 39-43 where it describes “The elements of a method, process, or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two.” Here, Khare establishes the method described causing a hardware or computer to execute the process. Further, see Khare in column 2 lines 22-39 where it describes “In accordance with an illustrative embodiment, a computer-implemented recommendation service determines a number of frequent attribute-value tuples based on historical item acquisition data (e.g., purchase histories or other item acquisition histories). An item may be considered acquired by a user when the item is purchased, rented, licensed, downloaded, installed, added to a wish list, saved, tagged, recommended, or subscribed to by the user. Illustratively, the recommendation service may employ various frequent itemset mining methods to identify meaningful frequent attribute-value tuples. As described above, the mining methods can be applied to transactions represented by attribute-values rather than items. Thresholds or other criteria can be used to configure the frequent attribute-value tuple mining process. For example, any frequent attribute-value tuple must correspond to a minimum number of distinct transactions or a minimum number of distinct users associated with the transactions.” Here, Khare establishes obtaining a history of information from users which are in relation to transactions. Khare also establishes the first tendency with the frequent attribute tuple which is based on transactions made by a distinct number of users which is a plurality Further, see Khare in column 2 lines 40-54 describing “The recommendation service may associate user interest measures with individual frequent attribute-value tuples. For example, a user's explicit rating of an item can be converted to the user's interest measure for a frequent attribute-value tuple that corresponds to the item. Alternatively or in addition, a user interest measure for a particular frequent attribute-value tuple can be derived from the user's interactions (e.g., past purchases) with items corresponding to the particular frequent attribute-value tuple.” Here, Khare establishes a singular user’s interest measure in relation to selection frequency. Further see Khare in column 7 lines 23-31 describing “Illustratively, the recommendation service 150 may examine user interest measures that link the target user to frequent attribute-value tuples as generated by the routine of FIG. 3 (e.g., by parsing a row of data corresponding to the target user in one or more matrices of user interest measures as maintained by the frequent attribute database 173), and may sort or order frequent attribute-value tuples into a list based on their corresponding user interest measures with respect to the target user.” Here this can be seen as the second tendency in relation to the predetermined target user as it uses a singular user’s interest measures.
Further, Khare teaches, “specifying an attribute that includes a difference equal to or greater than a predetermined value in the selection frequency of each attribute by comparing the first tendency and the second tendency;”
See Khare in column 2 lines 55-63 where it describes “The recommendation service may then generate recommendations for any individual user based on the frequent attribute-value tuples and the user interest measures. For example, the recommendation service may identify one or more frequent attribute-value tuples associated with sufficiently high user interest measures (e.g., above a predetermined threshold) with respect to a particular user, and use these frequent attribute-value tuples as a basis for recommending items to the particular user.” Here Khare teaches specifying an attribute with the generation of a recommendation which is based on the frequent attribute-value tuple and the user interest measures. In the previous limitation we established that the tuples could be seen as a first tendency and the user interest measures are associated with the second tendency. The difference equal to or greater than a predetermined value is the same as being above a predetermined threshold which is obtained by the comparison of the tendencies which is taught here.
However, Khare did not explicitly teach “when generating data to be used for one of creating and updating a model that corresponds to a rule of an attribute presented to the target user by integrating the first tendency and the second tendency, performing integration by using the second tendency for the specified attribute.”
Further, D’Alessandro in the same field of art teaches, “when generating data to be used for one of creating and updating a model that corresponds to a rule of an attribute presented to the target user by integrating the first tendency and the second tendency, performing integration by using the second tendency for the specified attribute.”
See D’Alessandro in paragraph [0006] where it describes “a non-transitory computer readable storage medium having embodied thereon a program is provided. The program is executable by a processor to perform a method of automated account interaction.” Here, D’Alessandro establishes the non-transitory computer readable storage medium to execute a process. Further see D’Alessandro in paragraph [0030] where it describes “The system uses the trained ML models to generate a recommended transaction at least in part by inputting the intent for the transaction to the trained ML models. In some examples, the training data for the one or more ML models can include historical information, an intent for the transaction identified in the historical information, and/or a second transaction performed by the user after the transaction identified in the historical information. The second transaction may be related to the intent and/or to the transaction identified in the historical information. During a validation stage of training, the system can use the one or more ML models to generate a recommended transaction based on the intent and/or based on the historical information, and the system can update and/or further train the one or more ML models based on whether or not the recommended transaction matches the second transaction from the training data.” Here D’Alessandro establishes data used to update a ML model based on a recommended transaction and second transaction which can be seen as integration of a first tendency and second tendency in an analogous system and the recommended transaction can be treated as the integrated second tendency due to it being the specified transaction. In this, the recommended transaction can be seen as the rule of an attribute presented to the target user, the recommended transaction includes an identified intent for the transaction and this identified intent dome by the system is being interpreted as the technique or rule of an attribute.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Khare with the teachings of D’Alessandro by using Khare’s teachings of obtaining tendencies and specifying an attribute, and incorporate with D’Alessandro’s teaching of performing integration.
One of ordinary skill in the art would be motivated to do so because by integrating D’Alessandro’s frameworks into the methods of Khare, one with ordinary skill in the art would “provide customized recommendations (e.g., recommended transactions) that are customized and/or tailored specifically to users based on their histories, their account information, intents determined behind their transaction(s), or combinations thereof. This improves over systems that are unable to provide recommendations, or provide standardized recommendations without such customization.” (D’Alessandro, paragraph [0034]).
Claim 6:
Regarding claim 6, Khare in view of D’Alessandro teaches the limitations in claim 4.
Further D’Alessandro teaches, “The rule formation support method according to claim 4, wherein the history information indicates a history of attributes assigned to document information forwarded between users, and wherein, in the performing integration, data to be used for one of creating and updating a model that corresponds to a rule for presenting an attribute assigned when the target user forwards the document information is generated.”
See D’Alessandro in paragraph [0003] where it describes “The historical information can include, for example, demographic data, transaction histories, credit histories, account histories of the account, characteristics of the user, actions performed by the user and/or using the user account, and the like.” Here, D’Alessandro establishes the historical information which can consist of document information indicating a history of different attributes. Further see D’Alessandro again in paragraph [0003] where it describes “The system uses the one or more trained machine learning models to identify an intent for the transaction at least in part by inputting the historical information to the one or more trained machine learning models. The system provides the intent for the transaction to the one or more trained machine learning models. The system uses the trained machine learning models to generate a recommended transaction at least in part by inputting the intent for the transaction to the trained machine learning models.” Here D’Alessandro establishes document information being forwarded by inputting the historical information to the ML model which updates a model corresponding to a target user forwarding document information, data is then generated by the recommended transaction. As mentioned in previous limitations, the recommended transaction includes an identified intent for the transaction and this identified intent dome by the system is being interpreted as the technique or rule of an attribute. Further see D’Alessandro in paragraph [0036] where it describes “In some cases, user device 120 may receive an input from a user of the user device 120.” Here D’Alessandro establishes a user device being able to receive an input from a user. Further see D’Alessandro in paragraph [0054] where it describes “In some examples, an agent device 260 may communicate with a user device 120, either directly or through the user front-end 202, the agent back-end 210, the automation engine 216, the web server(s) 208, or a combination thereof. For example, an agent using the agent device 260 may communicate with a user using the user device 120. The agent may be a human operator or an artificial intelligence (AI) assistant.” Here D’Alessandro establishes a communication between two users which can be linked two document information being forwarded between a user and an agent or other user and the user can input information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Khare with the teachings of D’Alessandro by using Khare’s teachings of obtaining tendencies and specifying an attribute, and incorporate with D’Alessandro’s teaching of performing integration.
One of ordinary skill in the art would be motivated to do so because by integrating D’Alessandro’s frameworks into the methods of Khare, one with ordinary skill in the art would “provide customized recommendations (e.g., recommended transactions) that are customized and/or tailored specifically to users based on their histories, their account information, intents determined behind their transaction(s), or combinations thereof. This improves over systems that are unable to provide recommendations, or provide standardized recommendations without such customization.” (D’Alessandro, paragraph [0034]).
Claim 7:
Regarding claim 7, Khare teaches “obtain, based on history information of transactions by each user, a first tendency that indicates a selection frequency of each attribute in the transactions by a plurality of users and a second tendency that indicates a selection frequency of each attribute in the transactions by a predetermined target user;”
See Khare in column 2 lines 22-39 where it describes “In accordance with an illustrative embodiment, a computer-implemented recommendation service determines a number of frequent attribute-value tuples based on historical item acquisition data (e.g., purchase histories or other item acquisition histories). An item may be considered acquired by a user when the item is purchased, rented, licensed, downloaded, installed, added to a wish list, saved, tagged, recommended, or subscribed to by the user. Illustratively, the recommendation service may employ various frequent itemset mining methods to identify meaningful frequent attribute-value tuples. As described above, the mining methods can be applied to transactions represented by attribute-values rather than items. Thresholds or other criteria can be used to configure the frequent attribute-value tuple mining process. For example, any frequent attribute-value tuple must correspond to a minimum number of distinct transactions or a minimum number of distinct users associated with the transactions.” Here, Khare establishes obtaining a history of information from users which are in relation to transactions. Khare also establishes the first tendency with the frequent attribute tuple which is based on transactions made by a distinct number of users which is a plurality Further, see Khare in column 2 lines 40-54 describing “The recommendation service may associate user interest measures with individual frequent attribute-value tuples. For example, a user's explicit rating of an item can be converted to the user's interest measure for a frequent attribute-value tuple that corresponds to the item. Alternatively or in addition, a user interest measure for a particular frequent attribute-value tuple can be derived from the user's interactions (e.g., past purchases) with items corresponding to the particular frequent attribute-value tuple.” Here, Khare establishes a singular user’s interest measure in relation to selection frequency. Further see Khare in column 7 lines 23-31 describing “Illustratively, the recommendation service 150 may examine user interest measures that link the target user to frequent attribute-value tuples as generated by the routine of FIG. 3 (e.g., by parsing a row of data corresponding to the target user in one or more matrices of user interest measures as maintained by the frequent attribute database 173), and may sort or order frequent attribute-value tuples into a list based on their corresponding user interest measures with respect to the target user.” Here this can be seen as the second tendency in relation to the predetermined target user as it uses a singular user’s interest measures.
Further, Khare teaches, “specify an attribute that includes a difference equal to or greater than a predetermined value in the selection frequency of each attribute by comparing the first tendency and the second tendency;”
See Khare in column 2 lines 55-63 where it describes “The recommendation service may then generate recommendations for any individual user based on the frequent attribute-value tuples and the user interest measures. For example, the recommendation service may identify one or more frequent attribute-value tuples associated with sufficiently high user interest measures (e.g., above a predetermined threshold) with respect to a particular user, and use these frequent attribute-value tuples as a basis for recommending items to the particular user.” Here Khare teaches specifying an attribute with the generation of a recommendation which is based on the frequent attribute-value tuple and the user interest measures. In the previous limitation we established that the tuples could be seen as a first tendency and the user interest measures are associated with the second tendency. The difference equal to or greater than a predetermined value is the same as being above a predetermined threshold which is obtained by the comparison of the tendencies which is taught here.
However, Khare did not explicitly teach “A rule formation support apparatus comprising: a memory; and a processor couple to the memory and when generating data to be used for one of creating and updating a model that corresponds to a rule of an attribute presented to the target user by integrating the first tendency and the second tendency, perform integration by using the second tendency for the specified attribute.”
Further, D’Alessandro in the same field of art teaches, “A rule formation support apparatus comprising: a memory; and a processor couple to the memory and when generating data to be used for one of creating and updating a model that corresponds to a rule of an attribute presented to the target user by integrating the first tendency and the second tendency, perform integration by using the second tendency for the specified attribute.”
See D’Alessandro in paragraph [0006] where it describes “a non-transitory computer readable storage medium having embodied thereon a program is provided. The program is executable by a processor to perform a method of automated account interaction.” Here, D’Alessandro establishes the non-transitory computer readable storage medium to execute a process. Further see D’Alessandro in paragraph [0030] where it describes “The system uses the trained ML models to generate a recommended transaction at least in part by inputting the intent for the transaction to the trained ML models. In some examples, the training data for the one or more ML models can include historical information, an intent for the transaction identified in the historical information, and/or a second transaction performed by the user after the transaction identified in the historical information. The second transaction may be related to the intent and/or to the transaction identified in the historical information. During a validation stage of training, the system can use the one or more ML models to generate a recommended transaction based on the intent and/or based on the historical information, and the system can update and/or further train the one or more ML models based on whether or not the recommended transaction matches the second transaction from the training data.” Here D’Alessandro establishes data used to update a ML model based on a recommended transaction and second transaction which can be seen as integration of a first tendency and second tendency in an analogous system and the recommended transaction can be treated as the integrated second tendency due to it being the specified transaction. In this, the recommended transaction can be seen as the rule of an attribute presented to the target user, the recommended transaction includes an identified intent for the transaction and this identified intent dome by the system is being interpreted as the technique or rule of an attribute.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Khare with the teachings of D’Alessandro by using Khare’s teachings of obtaining tendencies and specifying an attribute, and incorporate with D’Alessandro’s teaching of performing integration.
One of ordinary skill in the art would be motivated to do so because by integrating D’Alessandro’s frameworks into the methods of Khare, one with ordinary skill in the art would “provide customized recommendations (e.g., recommended transactions) that are customized and/or tailored specifically to users based on their histories, their account information, intents determined behind their transaction(s), or combinations thereof. This improves over systems that are unable to provide recommendations, or provide standardized recommendations without such customization.” (D’Alessandro, paragraph [0034]).
Claim 9:
Regarding claim 9, Khare in view of D’Alessandro teaches the limitations in claim 7.
Further D’Alessandro teaches, “The rule formation support apparatus according to claim 7, wherein the history information indicates a history of attributes assigned to document information forwarded between users, and wherein the processor is configured to generate data to be used for one of creating and updating a model that corresponds to a rule for presenting an attribute assigned when the target user forwards the document information.”
See D’Alessandro in paragraph [0003] where it describes “The historical information can include, for example, demographic data, transaction histories, credit histories, account histories of the account, characteristics of the user, actions performed by the user and/or using the user account, and the like.” Here, D’Alessandro establishes the historical information which can consist of document information indicating a history of different attributes. Further see D’Alessandro again in paragraph [0003] where it describes “The system uses the one or more trained machine learning models to identify an intent for the transaction at least in part by inputting the historical information to the one or more trained machine learning models. The system provides the intent for the transaction to the one or more trained machine learning models. The system uses the trained machine learning models to generate a recommended transaction at least in part by inputting the intent for the transaction to the trained machine learning models.” Here D’Alessandro establishes document information being forwarded by inputting the historical information to the ML model which updates a model corresponding to a target user forwarding document information, data is then generated by the recommended transaction. As mentioned in previous limitations, the recommended transaction includes an identified intent for the transaction and this identified intent dome by the system is being interpreted as the technique or rule of an attribute. Further see D’Alessandro in paragraph [0036] where it describes “In some cases, user device 120 may receive an input from a user of the user device 120.” Here D’Alessandro establishes a user device being able to receive an input from a user. Further see D’Alessandro in paragraph [0054] where it describes “In some examples, an agent device 260 may communicate with a user device 120, either directly or through the user front-end 202, the agent back-end 210, the automation engine 216, the web server(s) 208, or a combination thereof. For example, an agent using the agent device 260 may communicate with a user using the user device 120. The agent may be a human operator or an artificial intelligence (AI) assistant.” Here D’Alessandro establishes a communication between two users which can be linked two document information being forwarded between a user and an agent or other user and the user can input information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Khare with the teachings of D’Alessandro by using Khare’s teachings of obtaining tendencies and specifying an attribute, and incorporate with D’Alessandro’s teaching of performing integration.
One of ordinary skill in the art would be motivated to do so because by integrating D’Alessandro’s frameworks into the methods of Khare, one with ordinary skill in the art would “provide customized recommendations (e.g., recommended transactions) that are customized and/or tailored specifically to users based on their histories, their account information, intents determined behind their transaction(s), or combinations thereof. This improves over systems that are unable to provide recommendations, or provide standardized recommendations without such customization.” (D’Alessandro, paragraph [0034]).
Claim(s) 2, 5, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Khare T. et al, in view of D'Alessandro A. et al, and further in view of Kawamoto Y. et al, (US. Patent Application Publication 20140012862 A1) filed on May 28, 2013, (hereafter Kawamoto).
Claim 2:
Regarding claim 2, Khare in view of D’Alessandro teaches the limitations in claim 1.
Khare teaches, “The non-transitory computer-readable recording medium according to claim 1 wherein, in the specifying, a value obtained by…selection frequency of each attribute in the first tendency is compared with a value obtained by…selection frequency of each attribute in the second tendency, and the attribute that includes a difference equal to or greater than a predetermined value between the…values is specified.”
See Khare in column 2 lines 55-63 where it describes “The recommendation service may then generate recommendations for any individual user based on the frequent attribute-value tuples and the user interest measures. For example, the recommendation service may identify one or more frequent attribute-value tuples associated with sufficiently high user interest measures (e.g., above a predetermined threshold) with respect to a particular user, and use these frequent attribute-value tuples as a basis for recommending items to the particular user.” Here Khare teaches specifying an attribute with the generation of a recommendation which is based on the frequent attribute-value tuple and the user interest measures, which also establishes a comparison. In the previous limitation we established that the tuples could be seen as a first tendency and the user interest measures are associated with the second tendency. The difference equal to or greater than a predetermined value is the same as being above a predetermined threshold which is obtained by the comparison of the tendencies which is taught here. Further, See Khare in column 2 lines 22-39 where it describes “In accordance with an illustrative embodiment, a computer-implemented recommendation service determines a number of frequent attribute-value tuples based on historical item acquisition data (e.g., purchase histories or other item acquisition histories). An item may be considered acquired by a user when the item is purchased, rented, licensed, downloaded, installed, added to a wish list, saved, tagged, recommended, or subscribed to by the user. Illustratively, the recommendation service may employ various frequent itemset mining methods to identify meaningful frequent attribute-value tuples. As described above, the mining methods can be applied to transactions represented by attribute-values rather than items. Thresholds or other criteria can be used to configure the frequent attribute-value tuple mining process. For example, any frequent attribute-value tuple must correspond to a minimum number of distinct transactions or a minimum number of distinct users associated with the transactions.” Here, Khare also establishes the first tendency with the frequent attribute tuple which is based on transactions made by a distinct number of users which is a plurality. Further, see Khare in column 2 lines 40-54 describing “The recommendation service may associate user interest measures with individual frequent attribute-value tuples. For example, a user's explicit rating of an item can be converted to the user's interest measure for a frequent attribute-value tuple that corresponds to the item. Alternatively or in addition, a user interest measure for a particular frequent attribute-value tuple can be derived from the user's interactions (e.g., past purchases) with items corresponding to the particular frequent attribute-value tuple.” Here, Khare establishes a singular user’s interest measure in relation to selection frequency. Further see Khare in column 7 lines 23-31 describing “Illustratively, the recommendation service 150 may examine user interest measures that link the target user to frequent attribute-value tuples as generated by the routine of FIG. 3 (e.g., by parsing a row of data corresponding to the target user in one or more matrices of user interest measures as maintained by the frequent attribute database 173), and may sort or order frequent attribute-value tuples into a list based on their corresponding user interest measures with respect to the target user.” Here this can be seen as the second tendency in relation to the predetermined target user as it uses a singular user’s interest measures.
Neither Khare or D’Alessandro appears to teach the “a value obtained by normalizing the … frequency of each attribute in the first tendency is compared with a value obtained by normalizing the … frequency of each attribute in the second tendency, and the attribute that includes a difference equal to or greater than a predetermined value between the normalized values is specified.” However, Kawamoto in the same field of art teaches, “value obtained by normalizing the … frequency of each attribute in the first tendency is compared with a value obtained by normalizing the … frequency of each attribute in the second tendency, and the attribute that includes a difference equal to or greater than a predetermined value between the normalized values is specified”
See Kawamoto in paragraph [0013] describing “The generation unit may generate the sample data so that the first appearance frequency for each sample attribute value expressed by the frequency function and a second appearance frequency, which is an appearance frequency for each sample attribute value in the sample data are corresponded to each other.” Here, Kawamoto establishes a first and second appearance frequency which can be seen as first and second tendencies in an analogous system as they are also based on attributes. This also establishes a function and a comparison of the two frequencies. Further, see Kawamoto in paragraph [0133] describing, “Examples of the model function selected include an exponential function, a linear function, a logarithmetic function, a polynomial function, a gauss function, and the like. In this embodiment, the following gauss function is selected as the model function.
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”. In this Kawamoto establishes a model function. Further, see Kawamoto in paragraph [0136] describing “In this embodiment, the model function g(x) that has been subjected to the fitting is normalized, thereby calculating the frequency function f(x). Specifically, if the one or more attribute values 34b shown in FIG. 8 are represented by (y1 to ym), a normalized parameter k is determined so that k∑g(yi)=1 is obtained. For example, if m=15 and yi=152+2(i-1) are set, k=0.98 is obtained. As a result, as the frequency function f(x) for generating the pseudo sample data 50, kg(x) is obtained (f(x)=kg(x)).” Here, Kawamoto teaches normalizing a model function for fitting which then calculates a frequency function in respect to attribute values. The frequency functions as mentioned before is used for the first and second frequencies which can now be seen as normalizing a value for a first and second tendency. Further, see Kawamoto in paragraph [0184] describing “In an example shown in FIG. 13, the frequency function f(x) is calculated by fitting. Such an attribute value that a difference between the first appearance frequency (graph of FIG. 13) expressed by f(x) calculated once and a frequency of the attribute value x is larger than a predetermined value is set as the non-target attribute value 40.” Here, Kawamoto teaches applying the difference of a first appearance frequency or first tendency and another frequency or second tendency applying the function which is also for fitting, established to be normalized to see if it is greater than a predetermined threshold, to then set an attribute value which can be seen as specifying an attribute.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Khare with the teachings of D’Alessandro and Kawamoto by using Khare’s teachings of obtaining tendencies and specifying an attribute, and incorporate with D’Alessandro’s teaching of performing integration, and further Kawamoto’s teaching of normalizing a selection frequency for specifying an attribute.
One of ordinary skill in the art would be motivated to do so because by integrating Kawamoto’s frameworks into the methods of Khare and D’Alessandro, one with ordinary skill in the art would bring a “an information processing apparatus including a calculation unit and a generation unit” (Kawamoto, paragraph [0007]), “The calculation unit is configured to calculate a frequency function which is a function relating to an appearance frequency of one or more attribute values of a database having a predetermined attribute and the one or more attribute values relating to the attribute” (Kawamoto, paragraph [0008]), and “The generation unit is configured to generate sample data in accordance with the appearance frequency relating to the database on the basis of the frequency function calculated, the sample data including at least a part of the one or more attribute values as one or more sample attribute values” (Kawamoto, paragraph [0009]). This calculation and generation unit in turn will assist to achieve the result of what is claimed here and further provides functions for normalizing values of attributes.
Claim 5:
Regarding claim 5, Khare in view of D’Alessandro teaches the limitations in claim 4.
Khare teaches “The rule formation support method according to claim 4, wherein, in the specifying, a value obtained by…selection frequency of each attribute in the first tendency is compared with a value obtained by…selection frequency of each attribute in the second tendency, and the attribute that includes a difference equal to or greater than a predetermined value between the…values is specified.”
See Khare in column 2 lines 55-63 where it describes “The recommendation service may then generate recommendations for any individual user based on the frequent attribute-value tuples and the user interest measures. For example, the recommendation service may identify one or more frequent attribute-value tuples associated with sufficiently high user interest measures (e.g., above a predetermined threshold) with respect to a particular user, and use these frequent attribute-value tuples as a basis for recommending items to the particular user.” Here Khare teaches specifying an attribute with the generation of a recommendation which is based on the frequent attribute-value tuple and the user interest measures, which also establishes a comparison. In the previous limitation we established that the tuples could be seen as a first tendency and the user interest measures are associated with the second tendency. The difference equal to or greater than a predetermined value is the same as being above a predetermined threshold which is obtained by the comparison of the tendencies which is taught here. Further, See Khare in column 2 lines 22-39 where it describes “In accordance with an illustrative embodiment, a computer-implemented recommendation service determines a number of frequent attribute-value tuples based on historical item acquisition data (e.g., purchase histories or other item acquisition histories). An item may be considered acquired by a user when the item is purchased, rented, licensed, downloaded, installed, added to a wish list, saved, tagged, recommended, or subscribed to by the user. Illustratively, the recommendation service may employ various frequent itemset mining methods to identify meaningful frequent attribute-value tuples. As described above, the mining methods can be applied to transactions represented by attribute-values rather than items. Thresholds or other criteria can be used to configure the frequent attribute-value tuple mining process. For example, any frequent attribute-value tuple must correspond to a minimum number of distinct transactions or a minimum number of distinct users associated with the transactions.” Here, Khare also establishes the first tendency with the frequent attribute tuple which is based on transactions made by a distinct number of users which is a plurality. Further, see Khare in column 2 lines 40-54 describing “The recommendation service may associate user interest measures with individual frequent attribute-value tuples. For example, a user's explicit rating of an item can be converted to the user's interest measure for a frequent attribute-value tuple that corresponds to the item. Alternatively or in addition, a user interest measure for a particular frequent attribute-value tuple can be derived from the user's interactions (e.g., past purchases) with items corresponding to the particular frequent attribute-value tuple.” Here, Khare establishes a singular user’s interest measure in relation to selection frequency. Further see Khare in column 7 lines 23-31 describing “Illustratively, the recommendation service 150 may examine user interest measures that link the target user to frequent attribute-value tuples as generated by the routine of FIG. 3 (e.g., by parsing a row of data corresponding to the target user in one or more matrices of user interest measures as maintained by the frequent attribute database 173), and may sort or order frequent attribute-value tuples into a list based on their corresponding user interest measures with respect to the target user.” Here this can be seen as the second tendency in relation to the predetermined target user as it uses a singular user’s interest measures.
Neither Khare or D’Alessandro appears to teach the “value obtained by normalizing the … frequency of each attribute in the first tendency is compared with a value obtained by normalizing the … frequency of each attribute in the second tendency, and the attribute that includes a difference equal to or greater than a predetermined value between the normalized values is specified.” However, Kawamoto in the same field of art teaches, “value obtained by normalizing the … frequency of each attribute in the first tendency is compared with a value obtained by normalizing the … frequency of each attribute in the second tendency, and the attribute that includes a difference equal to or greater than a predetermined value between the normalized values is specified”
See Kawamoto in paragraph [0013] describing “The generation unit may generate the sample data so that the first appearance frequency for each sample attribute value expressed by the frequency function and a second appearance frequency, which is an appearance frequency for each sample attribute value in the sample data are corresponded to each other.” Here, Kawamoto establishes a first and second appearance frequency which can be seen as first and second tendencies in an analogous system as they are also based on attributes. This also establishes a function and a comparison of the two frequencies. Further, see Kawamoto in paragraph [0133] describing, “Examples of the model function selected include an exponential function, a linear function, a logarithmetic function, a polynomial function, a gauss function, and the like. In this embodiment, the following gauss function is selected as the model function.
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”. In this Kawamoto establishes a model function. Further, see Kawamoto in paragraph [0136] describing “In this embodiment, the model function g(x) that has been subjected to the fitting is normalized, thereby calculating the frequency function f(x). Specifically, if the one or more attribute values 34b shown in FIG. 8 are represented by (y1 to ym), a normalized parameter k is determined so that k∑g(yi)=1 is obtained. For example, if m=15 and yi=152+2(i-1) are set, k=0.98 is obtained. As a result, as the frequency function f(x) for generating the pseudo sample data 50, kg(x) is obtained (f(x)=kg(x)).” Here, Kawamoto teaches normalizing a model function for fitting which then calculates a frequency function in respect to attribute values. The frequency functions as mentioned before is used for the first and second frequencies which can now be seen as normalizing a value for a first and second tendency. Further, see Kawamoto in paragraph [0184] describing “In an example shown in FIG. 13, the frequency function f(x) is calculated by fitting. Such an attribute value that a difference between the first appearance frequency (graph of FIG. 13) expressed by f(x) calculated once and a frequency of the attribute value x is larger than a predetermined value is set as the non-target attribute value 40.” Here, Kawamoto teaches applying the difference of a first appearance frequency or first tendency and another frequency or second tendency applying the function which is also for fitting, established to be normalized to see if it is greater than a predetermined threshold, to then set an attribute value which can be seen as specifying an attribute.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Khare with the teachings of D’Alessandro and Kawamoto by using Khare’s teachings of obtaining tendencies and specifying an attribute, and incorporate with D’Alessandro’s teaching of performing integration, and further Kawamoto’s teaching of normalizing a selection frequency for specifying an attribute.
One of ordinary skill in the art would be motivated to do so because by integrating Kawamoto’s frameworks into the methods of Khare and D’Alessandro, one with ordinary skill in the art would bring a “an information processing apparatus including a calculation unit and a generation unit” (Kawamoto, paragraph [0007]), “The calculation unit is configured to calculate a frequency function which is a function relating to an appearance frequency of one or more attribute values of a database having a predetermined attribute and the one or more attribute values relating to the attribute” (Kawamoto, paragraph [0008]), and “The generation unit is configured to generate sample data in accordance with the appearance frequency relating to the database on the basis of the frequency function calculated, the sample data including at least a part of the one or more attribute values as one or more sample attribute values” (Kawamoto, paragraph [0009]). This calculation and generation unit in turn will assist to achieve the result of what is claimed here and further provides functions for normalizing values of attributes.
Claim 8:
Regarding claim 8, Khare in view of D’Alessandro teaches the limitations in claim 7.
Khare teaches “The rule formation support apparatus according to claim 7, wherein the processor is configured to compare a value obtained by…selection frequency of each attribute in the first tendency with a value obtained by…the selection frequency of each attribute in the second tendency, and specify the attribute that includes a difference equal to or greater than a predetermined value between the normalized values.”
See Khare in column 2 lines 55-63 where it describes “The recommendation service may then generate recommendations for any individual user based on the frequent attribute-value tuples and the user interest measures. For example, the recommendation service may identify one or more frequent attribute-value tuples associated with sufficiently high user interest measures (e.g., above a predetermined threshold) with respect to a particular user, and use these frequent attribute-value tuples as a basis for recommending items to the particular user.” Here Khare teaches specifying an attribute with the generation of a recommendation which is based on the frequent attribute-value tuple and the user interest measures, which also establishes a comparison. In the previous limitation we established that the tuples could be seen as a first tendency and the user interest measures are associated with the second tendency. The difference equal to or greater than a predetermined value is the same as being above a predetermined threshold which is obtained by the comparison of the tendencies which is taught here. Further, See Khare in column 2 lines 22-39 where it describes “In accordance with an illustrative embodiment, a computer-implemented recommendation service determines a number of frequent attribute-value tuples based on historical item acquisition data (e.g., purchase histories or other item acquisition histories). An item may be considered acquired by a user when the item is purchased, rented, licensed, downloaded, installed, added to a wish list, saved, tagged, recommended, or subscribed to by the user. Illustratively, the recommendation service may employ various frequent itemset mining methods to identify meaningful frequent attribute-value tuples. As described above, the mining methods can be applied to transactions represented by attribute-values rather than items. Thresholds or other criteria can be used to configure the frequent attribute-value tuple mining process. For example, any frequent attribute-value tuple must correspond to a minimum number of distinct transactions or a minimum number of distinct users associated with the transactions.” Here, Khare also establishes the first tendency with the frequent attribute tuple which is based on transactions made by a distinct number of users which is a plurality. Further, see Khare in column 2 lines 40-54 describing “The recommendation service may associate user interest measures with individual frequent attribute-value tuples. For example, a user's explicit rating of an item can be converted to the user's interest measure for a frequent attribute-value tuple that corresponds to the item. Alternatively or in addition, a user interest measure for a particular frequent attribute-value tuple can be derived from the user's interactions (e.g., past purchases) with items corresponding to the particular frequent attribute-value tuple.” Here, Khare establishes a singular user’s interest measure in relation to selection frequency. Further see Khare in column 7 lines 23-31 describing “Illustratively, the recommendation service 150 may examine user interest measures that link the target user to frequent attribute-value tuples as generated by the routine of FIG. 3 (e.g., by parsing a row of data corresponding to the target user in one or more matrices of user interest measures as maintained by the frequent attribute database 173), and may sort or order frequent attribute-value tuples into a list based on their corresponding user interest measures with respect to the target user.” Here this can be seen as the second tendency in relation to the predetermined target user as it uses a singular user’s interest measures.
Neither Khare or D’Alessandro appears to teach the “value obtained by normalizing the … frequency of each attribute in the first tendency is compared with a value obtained by normalizing the … frequency of each attribute in the second tendency, and the attribute that includes a difference equal to or greater than a predetermined value between the normalized values is specified.” However, Kawamoto in the same field of art teaches, “value obtained by normalizing the … frequency of each attribute in the first tendency is compared with a value obtained by normalizing the … frequency of each attribute in the second tendency, and the attribute that includes a difference equal to or greater than a predetermined value between the normalized values is specified”
See Kawamoto in paragraph [0013] describing “The generation unit may generate the sample data so that the first appearance frequency for each sample attribute value expressed by the frequency function and a second appearance frequency, which is an appearance frequency for each sample attribute value in the sample data are corresponded to each other.” Here, Kawamoto establishes a first and second appearance frequency which can be seen as first and second tendencies in an analogous system as they are also based on attributes. This also establishes a function and a comparison of the two frequencies. Further, see Kawamoto in paragraph [0133] describing, “Examples of the model function selected include an exponential function, a linear function, a logarithmetic function, a polynomial function, a gauss function, and the like. In this embodiment, the following gauss function is selected as the model function.
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”. In this Kawamoto establishes a model function. Further, see Kawamoto in paragraph [0136] describing “In this embodiment, the model function g(x) that has been subjected to the fitting is normalized, thereby calculating the frequency function f(x). Specifically, if the one or more attribute values 34b shown in FIG. 8 are represented by (y1 to ym), a normalized parameter k is determined so that k∑g(yi)=1 is obtained. For example, if m=15 and yi=152+2(i-1) are set, k=0.98 is obtained. As a result, as the frequency function f(x) for generating the pseudo sample data 50, kg(x) is obtained (f(x)=kg(x)).” Here, Kawamoto teaches normalizing a model function for fitting which then calculates a frequency function in respect to attribute values. The frequency functions as mentioned before is used for the first and second frequencies which can now be seen as normalizing a value for a first and second tendency. Further, see Kawamoto in paragraph [0184] describing “In an example shown in FIG. 13, the frequency function f(x) is calculated by fitting. Such an attribute value that a difference between the first appearance frequency (graph of FIG. 13) expressed by f(x) calculated once and a frequency of the attribute value x is larger than a predetermined value is set as the non-target attribute value 40.” Here, Kawamoto teaches applying the difference of a first appearance frequency or first tendency and another frequency or second tendency applying the function which is also for fitting, established to be normalized to see if it is greater than a predetermined threshold, to then set an attribute value which can be seen as specifying an attribute.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the base reference of Khare with the teachings of D’Alessandro and Kawamoto by using Khare’s teachings of obtaining tendencies and specifying an attribute, and incorporate with D’Alessandro’s teaching of performing integration, and further Kawamoto’s teaching of normalizing a selection frequency for specifying an attribute.
One of ordinary skill in the art would be motivated to do so because by integrating Kawamoto’s frameworks into the methods of Khare and D’Alessandro, one with ordinary skill in the art would bring a “an information processing apparatus including a calculation unit and a generation unit” (Kawamoto, paragraph [0007]), “The calculation unit is configured to calculate a frequency function which is a function relating to an appearance frequency of one or more attribute values of a database having a predetermined attribute and the one or more attribute values relating to the attribute” (Kawamoto, paragraph [0008]), and “The generation unit is configured to generate sample data in accordance with the appearance frequency relating to the database on the basis of the frequency function calculated, the sample data including at least a part of the one or more attribute values as one or more sample attribute values” (Kawamoto, paragraph [0009]). This calculation and generation unit in turn will assist to achieve the result of what is claimed here and further provides functions for normalizing values of attributes.
Conclusion
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/HASSAN RAMADAN SESAY/Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
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