DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1, 3-7 and 10 are pending for examinations.
Claims 1, 3-7 and 10 are rejected under 35 U.S.C. §101.
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, 3-7 and 10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to A Judicial Exception without significantly more.
Independent Claim Analysis
As Claims 1:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
The Claim recites:
A computer-implemented method for an automated process for analyzing, improving, and monitoring a co-prosperity of members of a network in real time, comprising the steps of, comprising the steps of:
(a) compiling, in real time, a database of key attribute data associated with each member, of the plurality of members in the network, the key attribute data comprising an aggregation of computer data associated with peer survey and self-survey response data on relationship habits and success outcomes for each member,
wherein the key attribute data is generated by, and received from, a plurality of computers associated with each member in the network;
(b) based on key attribute data associated with relationship habit for each member, generating corresponding relationship habit profiling data for each member; and
(c) determining, based on the relationship habit profiling data for each member, a member relationship habit index for each member, wherein the member relationship habit index represents a numerical classification of each member’s group working style as it relates to working relationship habits and styles with other members in the networks;
(d)determining, for the network, a network relationship habit index by combining the member relationship habit index of each member in the network, wherein the network relationship habit index represents a degree to which all members of the network employ interdependent working habits;
(e) based on key attribute data associated with the success outcomes of each of each member, determining a member success index for each member, wherein the member success index is a numerical representation of a level of group-working benefit as it relates to benefits offered by each member to a success welfare of the network and success wellbeing of the network; and
(f) for each member, determining a member social contribution index reflecting each member's relative contribution to the network as reflected by performance and productivity contributed by each member to the network;
(g) determining a co-prosperity index for each member from the member success index and member social contribution index for each member, wherein the co-prosperity index indicates of a success level of each member, measured both in terms of benefits each member enjoys form working in the network and a level of each member’s contribution to the networks’ success;
(h) providing a prediction model initially created by calculating correlation coefficients defining causal relationships between at least one of the member relationship habit index and network relationship habit index, and the co-prosperity index, of each member, the prediction model being updated and improved by a machine-learning model, whereby the method further comprises training the model on the accumulated key attribute data;
(i) with the prediction model, for each member, determining a social relationship habit improvement program comprising at least one change for each member based on a predictive modelling of impact of changes to the relationship habit profiling data of each member, which changes are predicted to increase the co-prosperity index of each member by increasing one or more of the member social contribution index and the member success index;
(j) displaying, on a user interface, the social relationship habit improvement program to each member and measuring any resulting change in one or more of the member social contribution index and the member success index;
(k) if a difference between a predicted change in one or more of the member social contribution index and the member success index and a measured change exists for a given member, then identifying at least one variable causing any difference; and
(l) updating the prediction model.
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
Step 2A prong 1:
Limitations “(h) providing a prediction model initially created by calculating correlation coefficients defining causal relationships between at least one of the member relationship habit index and network relationship habit index, and the co-prosperity index, of each member, the prediction model being updated and improved by a machine-learning model, whereby the method further comprises training the model on the accumulated key attribute data;” is directed to a mathematical concepts group of abstract ideas. Mathematical concepts are defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations.
Limitations “(b) based on key attribute data associated with relationship habit for each member, generating corresponding relationship habit profiling data for each member; and
(c) determining, based on the relationship habit profiling data for each member, a member relationship habit index for each member, wherein the member relationship habit index represents a numerical classification of each member’s group working style as it relates to working relationship habits and styles with other members in the networks;
(d) determining, for the network, a network relationship habit index by combining the member relationship habit index of each member in the network, wherein the network relationship habit index represents a degree to which all members of the network employ interdependent working habits;
(e) based on key attribute data associated with the success outcomes of each of each member, determining a member success index for each member, wherein the member success index is a numerical representation of a level of group-working benefit as it relates to benefits offered by each member to a success welfare of the network and success wellbeing of the network; and
(f) for each member, determining a member social contribution index reflecting each member's relative contribution to the network as reflected by performance and productivity contributed by each member to the network;
(g) determining a co-prosperity index for each member from the member success index and member social contribution index for each member, wherein the co-prosperity index indicates of a success level of each member, measured both in terms of benefits each member enjoys form working in the network and a level of each member’s contribution to the networks’ success;
(i) with the prediction model, for each member, determining a social relationship habit improvement program comprising at least one change for each member based on a predictive modelling of impact of changes to the relationship habit profiling data of each member, which changes are predicted to increase the co-prosperity index of each member by increasing one or more of the member social contribution index and the member success index;
(j) measuring any resulting change in one or more of the member social contribution index and the member success index;
(k) if a difference between a predicted change in one or more of the member social contribution index and the member success index and a measured change exists for a given member, then identifying at least one variable causing any difference; and” is/are directed to a mental processes group of abstract idea. Mental processes are defined as concepts that can practically be performed in the human mind, or by a human using pen and paper as a physical aid. Examples of mental processes includes observations, evaluations, judgements and opinions.
These steps are considered mental processes group of abstract idea.
Step 2A prong 2:
Limitations “A computer-implemented method for an automated process for analyzing, improving, and monitoring a co-prosperity of members of a network in real time, comprising the steps of, comprising the steps of:
(a) compiling, in real time, a database of key attribute data associated with each member, of the plurality of members in the network, the key attribute data comprising an aggregation of computer data associated with peer survey and self-survey response data on relationship habits and success outcomes for each member,
wherein the key attribute data is generated by, and received from, a plurality of computers associated with each member in the network;
(j) displaying, on a user interface, , the social relationship habit improvement program to each member and” are insignificant extra solution activity. See MPEP §2106.05(g).
The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
Limitation “A computer-implemented method for an automated process for analyzing, improving, and monitoring a co-prosperity of members of a network in real time, comprising the steps of, comprising the steps of:
(l) updating the prediction model.” was considered insignificant extra solution activity in Step 2A, and thus it is reevaluated in Step 2B. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)).
Limitation “(a) compiling, in real time, a database of key attribute data associated with each member, of the plurality of members in the network, the key attribute data comprising an aggregation of computer data associated with peer survey and self-survey response data on relationship habits and success outcomes for each member, wherein the key attribute data is generated by, and received from, a plurality of computers associated with each member in the network;” was considered insignificant extra solution activity in Step 2A, and thus it is reevaluated in Step 2B. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is mere-data gathering (MPEP 2106.05(g)(iii. Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responds)).
Limitation “(j) displaying, on a user interface, the social relationship habit improvement program to each member and” was considered insignificant extra solution activity in Step 2A, and thus it is reevaluated in Step 2B. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by court case cited in 2106.05(d) as evidenced by HAWK TECHNOLOGY SYSTEMS, LLC, v. CASTLE RETAIL, LLC.
The claim is directed to mental processes group of abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
As Claims 10:
Step 1: Are the Claims to a process, machine, manufacture or composition of matter? Yes.
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. See the analysis below.
The Claim recites:
A computer system for automatically analyzing, improving, and monitoring in real time a co-prosperity of a network having a plurality of members, the computer system comprising:
at least one processor; and
at least one computer-readable storage medium operatively coupled to the at least one processor and comprising representation of at least one set of computer instructions that, when executed by said processor, causes the computer system to perform the operations of.
compiling, in real time, a database of key attribute data associated with each member, of the plurality of members in the network, the key attribute data comprising an aggregation of computer data associated with peer survey and self-survey response data on relationship habits and success outcomes for each member, wherein the key attribute data is generated by, and received from, a plurality of computers associated with each member in the network;
based on respective key attribute data associated with each member, generating relationship habit profiling data associated with one or more relationship habits of each member;
determining, based on the relationship habit profiling data for each member, a member relationship habit index for each member, wherein the member relationship habit index is a numerical classification of each member’s group-working style as it relates to working relationship habits and styles with other members in the network; and
for the network, determining a network relationship habit index by combining the member relationship habit index of each member in the network, wherein the network habit index represents the degree to which all members of the network employ interdependent working habits;
based on the key attribute data associated with a welfare and a wellbeing of each member, determining a member success index for each member, wherein the member success index is a numerical representation of the level of group-working benefit as it relates to benefits offered by each member to a success welfare of the network and a success wellbeing of the network; and
for each member, determining a member social contribution index reflecting each member's social contribution to the network as reflected by performance and productivity contributed by each member of the network;
determining a co-prosperity index for each member from the member success index and member contribution index for each member, wherein the co-prosperity index indicates of a success level of each member, measured both in terms of benefits each member enjoys from working in the network and a level of each member’s contribution to the network’s success;
providing a prediction model produced by calculating correlation coefficients defining causal relationships between the relationship habit profiling data and the co-prosperity index of each member, the prediction model being updated and improved by a machine-learning model, whereby the method further comprises training the model on the accumulated key attribute data;
with the prediction model, determining a social relationship habit improvement program comprising at least one change to a given member based on a predictive modelling of an impact of changes to a social habit profile of the given member, which changes are predicted to increase the co-prosperity index of that member by increasing one or more of given member social contribution index and the member success index; and
displaying, on a user interface, the social relationship habit improvement program to the given member and measuring any resulting change in one or more of the member social contribution index and the member success index,
wherein the prediction model is updated by identifying at least one variable causing a difference, if any, between a predicted change in one or more of the member social contribution index and the member success index and a measured change.
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Regarding the non-emphasized limitations:
Step 2A prong 1:
Limitations “providing a prediction model produced by calculating correlation coefficients defining causal relationships between the relationship habit profiling data and the co-prosperity index of each member, the prediction model being updated and improved by a machine-learning model, whereby the method further comprises training the model on the accumulated key attribute data;” is directed to a mathematical concepts group of abstract ideas. Mathematical concepts are defined as mathematical relationships, mathematical formulas or equations, or mathematical calculations.
Limitations “based on respective key attribute data associated with each member, generating relationship habit profiling data associated with one or more relationship habits of each member;
determining, based on the relationship habit profiling data for each member, a member relationship habit index for each member, wherein the member relationship habit index is a numerical classification of each member’s group-working style as it relates to working relationship habits and styles with other members in the network; and
for the network, determining a network relationship habit index by combining the member relationship habit index of each member in the network, wherein the network habit index represents the degree to which all members of the network employ interdependent working habits;
based on the key attribute data associated with a welfare and a wellbeing of each member, determining a member success index for each member, wherein the member success index is a numerical representation of the level of group-working benefit as it relates to benefits offered by each member to a success welfare of the network and a success wellbeing of the network; and
for each member, determining a member social contribution index reflecting each member's social contribution to the network as reflected by performance and productivity contributed by each member of the network;
determining a co-prosperity index for each member from the member success index and member contribution index for each member, wherein the co-prosperity index indicates of a success level of each member, measured both in terms of benefits each member enjoys from working in the network and a level of each member’s contribution to the network’s success;
with the prediction model, determining a social relationship habit improvement program comprising at least one change to a given member based on a predictive modelling of an impact of changes to a social habit profile of the given member, which changes are predicted to increase the co-prosperity index of that member by increasing one or more of given member social contribution index and the member success index; and
measuring any resulting change in one or more of the member social contribution index and the member success index,
wherein the prediction model is updated by identifying at least one variable causing a difference, if any, between a predicted change in one or more of the member social contribution index and the member success index and a measured change” is/are directed to a mental processes group of abstract idea. Mental processes are defined as concepts that can practically be performed in the human mind, or by a human using pen and paper as a physical aid. Examples of mental processes includes observations, evaluations, judgements and opinions.
These steps are considered mental processes group of abstract idea.
Step 2A prong 2:
Limitations “A computer system for automatically analyzing, improving, and monitoring in real time a co-prosperity of a network having a plurality of members, the computer system comprising:
at least one processor; and
at least one computer-readable storage medium operatively coupled to the at least one processor and comprising representation of at least one set of computer instructions that, when executed by said processor, causes the computer system to perform the operations of.
compiling, in real time, a database of key attribute data associated with each member, of the plurality of members in the network, the key attribute data comprising an aggregation of computer data associated with peer survey and self-survey response data on relationship habits and success outcomes for each member, wherein the key attribute data is generated by, and received from, a plurality of computers associated with each member in the network;
displaying, on a user interface, the social relationship habit improvement program to the given member and” are insignificant extra solution activity. See MPEP §2106.05(g).
The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that integrate the Judicial Exception into a practical application? No.
Limitation “at least one processor; and at least one computer-readable storage medium operatively coupled to the at least one processor and comprising representation of at least one set of computer instructions that, when executed by said processor, causes the computer system to perform the operations of:” was considered insignificant extra solution activity in Step 2A, and thus it is reevaluated in Step 2B. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)).
Limitation “compiling, in real time, a database of key attribute data associated with each member, of the plurality of members in the network, the key attribute data comprising an aggregation of computer data associated with peer survey and self-survey response data on relationship habits and success outcomes for each member, wherein the key attribute data is generated by, and received from, a plurality of computers associated with each member in the network;” was considered insignificant extra solution activity in Step 2A, and thus it is reevaluated in Step 2B. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is mere-data gathering (MPEP 2106.05(g)(iii. Presenting offers to potential customers and gathering statistics generated based on the testing about how potential customers responds)).
Limitation “displaying, on a user interface, in real time, the social relationship habit improvement program to the given member and” was considered insignificant extra solution activity in Step 2A, and thus it is reevaluated in Step 2B. The addition of insignificant extra-solution activity does not amount to an inventive concept, particularly when the activity is well-understood or conventional (MPEP 2106.05(d)). This appears to be well-understood, routine, conventional as evidenced by court case cited in 2106.05(d) as evidenced by HAWK TECHNOLOGY SYSTEMS, LLC, v. CASTLE RETAIL, LLC.
The claim is directed to mental processes group of abstract idea. The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Dependent Claim Analysis
As Claim 3, the Claim recites “wherein the generating corresponding relationship habit profiling data for each member comprises compiling the key attribute data comprising results from a self-survey from each member and surveys of other members regarding relationship habits of that member”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation “wherein the generating corresponding relationship habit profiling data for each member comprises compiling the key attribute data comprising results from a self-survey from each member and surveys of other members regarding relationship habits of that member” is directed to a mathematical concepts group of abstract idea. Prong 2: The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 4, the Claim recites “wherein the relationship habits of each member are characterized as being dependent, independent or interdependent”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation “wherein the relationship habits of each member are characterized as being dependent, independent or interdependent” is directed to a mathematical concepts group of abstract idea. Prong 2: The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 5, the Claim recites “wherein any survey for each member may be shortened or simplified based on prior survey results or previously determined causal relationship”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation “wherein any survey for each member may be shortened or simplified based on prior survey results or previously determined causal relationship” is directed to a mental processes group of abstract idea. Prong 2: The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 6, the Claim recites “wherein a development and delivery of the social relationship habit improvement program is implemented with an automated software agent”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: There is no additional abstract idea abstract idea. Prong 2: Limitation “wherein a development and delivery of the social relationship habit improvement program is implemented with an automated software agent” is directed to Mere Instruction to Apply an Exception (See MPEP 2106.05(f)). The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
As Claim 7, the Claim recites “wherein the method further comprises modifying causal relationships between the member and a network social relationship habit profiles and the co-prosperity index of each member based on an identification of an at least outlier and at least one additional variable which at least partly explains the outlier”
The non-emphasized limitations describe abstract processes while emphasized limitations recited additional limitation(s).
Step 2A: Are the Claims directed to a law of nature, a natural phenomenon (product of nature) or an abstract idea? Yes, the Claims is an abstract idea. Prong 1: The limitation “wherein the method further comprises modifying causal relationships between the member and a network social relationship habit profiles and the co-prosperity index of each member based on an identification of an at least outlier and at least one additional variable which at least partly explains the outlier” is directed to a mathematical concepts group of abstract idea. Prong 2: The Claim(s) does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B: Does the Claim recite additional elements that amount to significantly more than the Judicial Exception? No. The Claim is not patent eligible.
Response to Arguments
Rejections under 35 U.S.C. §112(a):
Applicant amended the Claims; therefore, 35 U.S.C. §112 rejection(s) on the Claims are respectfully withdrawn.
Rejections under 35 U.S.C. §101:
As Step 2A (Prong One), Applicant argues that training of a neural network do not necessarily involve mathematical relationship (bottom of page 7 in the remarks).
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Applicant’s arguments are not persuasive because the Claims specifically state that “providing a prediction model produced by calculating …”. Calculating is construed to be involve mathematical calculation and is an abstract idea. Other determining steps, which do not involve calculation, are capable of being performed with a mind.
As Step 2A (Prong One), Applicant argues that claimed invention(s) are directed to an improvement such as in Claim 1 of example 42 (storing textual patent data in non-standardized format and then converting, storing and sharing the updated data in the standardized format) (third and last paragraph of page 8 in the remarks).
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Applicants’ arguments are not persuasive because the specification does not disclose non-standardized format and standardized format. There is no evidence that claimed limitation 1(a) refers to non-standardized format. Also, there is no evidence that claimed limitation (1(b)-1(i)) refers to standardized format.
Conclusion
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/NHAT HUY T NGUYEN/Primary Examiner, Art Unit 2147