Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 04/29/2022 was filed. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claims 1, 2, and 10 are objected to because of the following informalities: British English spelling of words should be switched to the American English spelling. “Organising” should be changed to “organizing”. “Human-centred” should be changed to “human-centered”. Programme should be changed to program. “Channelled” should be changed to “channeled”. “Prioritisation” should be changed to “prioritization”. “Realisation” should be changed to “realization”.
Claim 4 is objected to because of the following informalities: the semicolon after “elements” should be removed as it creates an incomplete following sentence. A suggested fix would be to replace the semicolon and “and” with a comma.
Claim 8 is objected to because of the following informalities: “multiples data sources” should be changed to “multiple data sources” to be grammatically correct. The claim teaches a “combination” but only mentions “high quantifying a value” which is singular. What the “combination” is referring to is not apparently clear. A suggested fix would be to specify what kind of value or quantifying is being done.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-2 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. These limitations raise questions that were not satisfactorily answered.
Claim 1: performing analytics to prompt, promote and prescribe solutioning and risk mitigating actions and determining risk profile;
Claim 2: performing analytics to prescribe risk mitigating actions and determining risk profile;
How and what analytics are performed?
Claim 1: clustering and sequencing challenges to be solved relative to defined risk profile solution themes, previously successful solution attempts from across the crowd or community and severity of risk relative to impact of a failed solution on transformation value objectives;
Claim 2: clustering and sequencing tasks relative to defined risk profile;
What is a “defined risk profile solution theme” or “defined risk profile”?
Claims 3-4 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 3, the phrase "etc." renders the claim indefinite because it is unclear what “etc.” entails as part of the claimed invention. See MPEP § 2173.05(d). A recommended action is to remove “etc.”.
Regarding Claim 4, the phrase "such as" renders the claim indefinite because it is unclear whether the limitations following the phrase are part of the claimed invention. See MPEP § 2173.05(d). A recommended action is to replace “such as” with “comprising” or “including”.
Claim 8 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The term “high quantifying” in Claim 8 is a relative term which renders the claim indefinite. The term “high quantifying” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. As a result, the terms “combination” and “value of subsections” are rendered indefinite because the how the value or values of subsections are arrived at is unclear and what the combination consists of is unclear. For instance, the combination could be referring to multiple values or perhaps one value representative of multiple subsections.
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, 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite mental processes and mathematical concepts. This judicial exception is not integrated into a practical application because the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception, as explained below.
Step 1 for all Claims:
Claims 1-9 are directed to methods (processes). Claim 10 is a system with no tangible structure and is thus considered software per se which is not one of the four statutory categories of invention. Claim 11 is directed to a manufacture. Therefore, Claims 1-9, 11 are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Regarding Claim 1:
Step 2A, Prong 1:
A computer based method for organising big-data and extracting workstream parameters to expedite digital transformations, employing analytics to prompt, promote and predict better answers to complex challenges from cognitively diverse communities thus mitigating digital programme transformation risks using crowds of human-centred thinking, total knowledge sourcing, personalised skills enhancement, augmented problem analysis and immersive team solutioning that is channelled into a group consensus for better decision making, comprising: As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses organizing big-data and extracting workstream parameters which is making a judgement based upon analyzing data and knowledge from multiple sources which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
authenticating users and workstreams; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses authenticating a user which is making a judgement based upon authentication credentials provided by the user which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
performing analytics to prompt, promote and prescribe solutioning and risk mitigating actions and determining risk profile; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing analytics which is making an evaluation based upon analysis of knowledge bases and figuring out a solution while considering risks which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
clustering and sequencing challenges to be solved relative to defined risk profile solution themes, previously successful solution attempts from across the crowd or community and severity of risk relative to impact of a failed solution on transformation value objectives; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses clustering and sequencing challenges which is making an evaluation based upon the present challenges and then grouping and ordering the challenges based upon historical evidence which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
and determining best approaches to restructuring programme workstreams, team structures, task prioritisation and benefit realisation tracking. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses determining the best approaches which is making a judgement based upon tasks and workloads to improve efficiency which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2:
a cloud based hosting and analytical Al platform; This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The cloud-based hosting and platform is recited at a high-level of generality with no detail of the hosting and platform process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
connecting users and users operating programs, including internal and external knowledge systems, programme applications and collaboration devices via secure means to the platform; This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The connecting is recited at a high-level of generality with no detail of the connecting process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
parsing structured and unstructured data from the users operating programs; This limitation amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
extracting from the users operating programs sequence, volume and intensity of challenges, problems and tasks to determine optimum solutioning profiles; This limitation amounts to extra-solution activity of gathering data and outputting for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
compiling and presenting metric and graphical representations of the solutioning and problem solving landscape to mitigate risk; This limitation amounts to extra-solution activity of gathering data and outputting for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
a cloud based hosting and analytical Al platform; This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The cloud-based hosting and platform is recited at a high-level of generality with no detail of the hosting and platform process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
connecting users and users operating programs, including internal and external knowledge systems, programme applications and collaboration devices via secure means to the platform; As discussed above, the additional elements of connecting users and users operating programs which is recited at a high level of generality and amounts to extra-solution activity of transmitting data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
parsing structured and unstructured data from the users operating programs; As discussed above, the additional elements of data parsing which is recited at a high level of generality and amounts to extra-solution activity of transmitting data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
extracting from the users operating programs sequence, volume and intensity of challenges, problems and tasks to determine optimum solutioning profiles; As discussed above, the additional elements of extraction which is recited at a high level of generality and amounts to extra-solution activity of transmitting data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
compiling and presenting metric and graphical representations of the solutioning and problem solving landscape to mitigate risk; As discussed above, the additional elements of data compilation and presentation which is recited at a high level of generality and amounts to extra-solution activity of transmitting data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
Regarding Claim 2:
Step 2A, Prong 1:
A computer based method for organising big-data and extracting workstream parameters to expedite digital transformations, which method employs analytics solution that predicts and mitigates digital programme transformation risks using human-centred interventions around skills, team composition, leadership, communication and collaboration, comprising: As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses organizing big-data and extracting workstream parameters which is making a judgement based upon analyzing data and knowledge from multiple sources which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
authenticating users and workstreams; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses authenticating a user which is making a judgement based upon authentication credentials provided by the user which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
performing analytics to prescribe risk mitigating actions and determining risk profile; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses performing analytics which is making an evaluation based upon analysis of knowledge bases and figuring out a solution while considering risks which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
clustering and sequencing tasks relative to defined risk profile; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses clustering and sequencing challenges which is making an evaluation based upon the present challenges and then grouping and ordering the challenges based upon historical evidence which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
predicting gaps and flagging emerging risks continuously during the lifecycle of the programs; As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses predicting emerging risks which is making an opinion based upon analyzing the program lifecycle which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Step 2A, Prong 2:
providing a cloud based hosting and analytical Al platform; This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The providing of a cloud-based hosting and platform is recited at a high-level of generality with no detail of the providing process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
connecting users and users operating programs via secure means to the platform; This limitation amounts to extra-solution activity of gathering data and outputting for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
parsing structured and unstructured data from the users operating programs; This limitation amounts to extra-solution activity of gathering data and outputting for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
extracting from the users operating programs sequence, volume and intensity of tasks to determine optimum fulfilment profiles; This limitation amounts to extra-solution activity of gathering data and outputting for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
compiling and presenting metric and graphical representations to mitigate risk; This limitation amounts to extra-solution activity of gathering data and outputting for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
and presenting restructured workstreams. This limitation amounts to extra-solution activity of gathering data and outputting for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
providing a cloud based hosting and analytical Al platform; This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The providing of a cloud-based hosting and platform is recited at a high-level of generality with no detail of the providing process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
connecting users and users operating programs via secure means to the platform; As discussed above, the additional elements of connecting users and users operating programs which is recited at a high level of generality and amounts to extra-solution activity of transmitting data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
parsing structured and unstructured data from the users operating programs; As discussed above, the additional elements of data parsing which is recited at a high level of generality and amounts to extra-solution activity of transmitting data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
extracting from the users operating programs sequence, volume and intensity of tasks to determine optimum fulfilment profiles; As discussed above, the additional elements of extraction which is recited at a high level of generality and amounts to extra-solution activity of transmitting data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
compiling and presenting metric and graphical representations to mitigate risk; As discussed above, the additional elements of data compilation and presentation which is recited at a high level of generality and amounts to extra-solution activity of transmitting data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
and presenting restructured workstreams. As discussed above, the additional elements of presenting workstreams which is recited at a high level of generality and amounts to extra-solution activity of transmitting data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
Regarding Claim 3:
Step 2A, Prong 2:
A method according to claim 1, wherein the data offered is in the form of documents, text, images, video, media files, metadata etc. The limitation of data in multiple forms amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
A method according to claim 1, wherein the data offered is in the form of documents, text, images, video, media files, metadata etc. The limitation of data in multiple forms amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Regarding Claim 4:
Step 2A, Prong 2:
A method according to claim 3, wherein the parsing of the data extracts metadata such as subject matter, topics, elements; and tags with keywords, location and codes of inter-connections. The limitation of parsing data to extract metadata amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
A method according to claim 3, wherein the parsing of the data extracts metadata such as subject matter, topics, elements; and tags with keywords, location and codes of inter-connections. The limitation of parsing data to extract metadata amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Regarding Claim 5:
Step 2A, Prong 2:
A method according to claim 4, wherein the clustering is arranged with filters offered from the metadata and selectable by a user. This limitation amounts to extra-solution activity of gathering data and grouping data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
A method according to claim 4, wherein the clustering is arranged with filters offered from the metadata and selectable by a user. As discussed above, the additional elements of data gathering and grouping which is recited at a high level of generality and amounts to extra-solution activity of transmitting data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
Regarding Claim 6:
Step 2A, Prong 1:
A method according to claim 1, further comparing and quantifying a value of compatibility of said data or data subsections with the users' operating program sequence. As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. For example, this limitation encompasses comparing and quantifying a value which is making a judgement based upon observing data and their compatibilities which can be feasibly performed in the human mind (see MPEP 2106.04(a)(2)(III)).
Regarding Claim 7:
Step 2A, Prong 2:
A method according to claim 6, wherein the parsing comprises data from two or more sources. This limitation amounts to extra-solution activity of gathering data and outputting for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
A method according to claim 6, wherein the parsing comprises data from two or more sources. As discussed above, the additional elements of data gathering which is recited at a high level of generality and amounts to extra-solution activity of transmitting data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
Regarding Claim 8:
Step 2A, Prong 2:
A method according to claim 1, wherein the presenting comprises a combination of high quantifying a value of subsections from multiples data sources. This limitation amounts to extra-solution activity of gathering data and outputting for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
A method according to claim 1, wherein the presenting comprises a combination of high quantifying a value of subsections from multiples data sources. As discussed above, the additional elements of data gathering and presenting which is recited at a high level of generality and amounts to extra-solution activity of transmitting data. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping", and "storing and retrieving information in memory").
Regarding Claim 9:
Step 2A, Prong 2:
A method according to claim 1, wherein the method is for use in virtual environments, in online virtual worlds or metaverse. The limitation of the method for organizing big-data and workstream parameters amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Step 2B:
A method according to claim 1, wherein the method is for use in virtual environments, in online virtual worlds or metaverse. The limitation of the method for organizing big-data and workstream parameters amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use. As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Regarding Claim 11:
Step 2A, Prong 2:
A non-transitory computer-readable storage medium having stored thereon computer-readable code, which, when executed by computing apparatus, causes the computing apparatus to perform the method of claim 1. This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The execution is recited at a high-level of generality with no detail of the execution of the computing process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Step 2B:
A non-transitory computer-readable storage medium having stored thereon computer-readable code, which, when executed by computing apparatus, causes the computing apparatus to perform the method of claim 1. This limitation is recited at a high level of generality and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. The execution is recited at a high-level of generality with no detail of the execution of the computing process such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
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.
Claims 1-8, 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Biswas (US 12079737 B1) in view of Malvankar et al. (US 11789774 B2), hereinafter referred to as Biswas and Malvankar, respectively.
Regarding Claim 1:
Biswas teaches: A computer based method for organising big-data and extracting workstream parameters to expedite digital transformations, employing analytics to prompt, promote and predict better answers to complex challenges from cognitively diverse communities thus mitigating digital programme transformation risks using crowds of human-centred thinking, total knowledge sourcing, personalised skills enhancement, augmented problem analysis and immersive team solutioning that is channelled into a group consensus for better decision making, comprising: (It is noted that these limitations amount to intended use. A recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim.
In addition, when reading the preamble in the context of the entire claim, the recitations are not limiting because the body of the claim describes a complete invention and the language recited solely in the preamble does not provide any distinct definition of any of the claimed invention’s limitations. Thus, the preamble of the claim(s) is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02.
a cloud based hosting and analytical Al platform; (Col 2 Lines 65-67 “The present disclosure discloses one or more aspects of data-mining and AI workflow platform for structured and unstructured data.”
Col 29 Lines 45-49 “The core web service 104 is configured to share the AI model as at least one of a web service, and an application to a client of the user. The core web service 104 shares the AI model by deploying the AI model on a cloud server and/or a physical server.”)
connecting users and users operating programs, including internal and external knowledge systems, programme applications and collaboration devices via secure means to the platform; (Col 26 Lines 23-30 “The system comprises a server, a device 102, and a database. The device is associated with a user. The server may communicate with the device 102 through a wired connection and/or a wireless connection. The user interacts with the server through a web service and/or an application accessed via the device 102. The server provides a user interface to the user through at least one of the web services, and/or the application.”
Examiner’s Note: Database is read as knowledge system. The server, read as the platform, is connected to users through user devices and the user interface/applications/web services that are ran on those user devices.)
authenticating users and workstreams; (Col 26 Lines 39-44; Lines 52-57 “The authentication service authenticates and authorizes the user to interact with the server upon providing user credentials. The core web service 104 allows the user to interact with the server upon the authentication and the authorization of the user is enabled by the user. The core web service 104 receive data from a data source.”
Examiner’s Note: The user’s interactions and input are read as workstream.)
parsing structured and unstructured data from the users operating programs; (Col 3 Lines 1-15 “The method comprises receiving data from a data source, wherein the data comprises at least one data format of at least one of structured data, semi-structured data and unstructured data; indexing and analyzing the received data; scheduling and uploading automatically the data to a database as per the indexing; visualizing the data and determining at least one of the structured data, the semi-structured data, and the unstructured data from the data uploaded; cleansing and filtering the data based on at least one of an input from a user, and a predefined rule;”
Examiner’s Note: The data that is structured, semi-structured, and unstructured comes from user input, which is read as users operating programs. The cleansing, filtering, indexing, and analysis of the data is read as parsing.)
extracting from the users operating programs sequence, volume and intensity of challenges, problems and tasks to determine optimum solutioning profiles; (Col 24 Lines 59-67, Col 25 Lines 1-6 “Under the dashboard and visualization service, the server provides data data preparation and visualizations under which data visualizations play a key role in preparing the data for machine learning. Data visualizations service/tool is configured to investigate and analyze the data for valuable insights, enabling your analyses to be more targeted and precise. The data visualizations tool empowers the user with a stronger data-intuition, which will help the user to identify the right AI model. The dashboard and visualization tools further perform passive data monitoring and exploration. The server passively monitors the data and generates a more holistic view of the user's network and informs the user of potential issues that may be directly affecting.”
Col 25 Lines 21-26 “The AI models assesses whether the data meets complex quality and compliance metrics and flag high-risk data accordingly and generate above-mentioned insights. The AI model by performing all these insights empower the user (e.g. a reviewer, a regulatory analyst, etc.) to quickly pinpoint and take corrective actions.”
Examiner’s Note: The data being received from user input is read as users operating programs. The investigation and analysis of the data though visualization is read as extraction. Generating a holistic view of the user’s network and informing of potential issues is read as extracting sequence, volume and intensity of the challenges/problems. Taking corrective actions is determining the optimum solution profiles.)
performing analytics to prompt, promote and prescribe solutioning and risk mitigating actions and determining risk profile; (Col 24 Lines 59-67, Col 25 Lines 1-6 “Under the dashboard and visualization service, the server provides data data preparation and visualizations under which data visualizations play a key role in preparing the data for machine learning. Data visualizations service/tool is configured to investigate and analyze the data for valuable insights, enabling your analyses to be more targeted and precise. The data visualizations tool empowers the user with a stronger data-intuition, which will help the user to identify the right AI model. The dashboard and visualization tools further perform passive data monitoring and exploration. The server passively monitors the data and generates a more holistic view of the user's network and informs the user of potential issues that may be directly affecting.”
Examiner’s Note: The server analyzing data and providing data visualizations to help the user identify the right AI model is read as performing analytics to prompt, promote, and prescribe solutioning. Informing the user of potential issues is read as risk mitigating action. Combining that with generating a holistic view of the user’s network is read as determining a risk profile.)
clustering and sequencing challenges to be solved relative to defined risk profile solution themes, previously successful solution attempts from across the crowd or community and severity of risk relative to impact of a failed solution on transformation value objectives; (Col 26 Lines 1-11 “The AI-enhanced RPA in the Real World finds application in port and border security. While scanning nowadays can be automated with a little programming, that data must still be manually processed and assessed for risk. The AI model, however, this overwhelming task becomes infinitely quicker, intuitive, and powerfully predictive. Rather than manually reviewing 26,000 container scans per day one-by-one, the AI-integrated RPA solution can be customized to generate a report indicating which of the 26,000 containers are likely to be high, medium, and low risk. The time previously spent by CBP staff and agents to conduct this same risk assessment can now be converted into time conducting investigations.”
Examiner’s Note: In this example, the defined risk profile is maintaining port and border security. Port and border security are read as previously successful solution attempts from across the community. Furthermore, the AI automates labeling the risk severity level for each container, AKA clustering challenges to be solved, with each container being a potential investigation for staff to operate on. The containers are effectively clustered and sequenced based on their high, medium, or low risk level based on border security principles and consequences of investigative failure.
compiling and presenting metric and graphical representations of the solutioning and problem solving landscape to mitigate risk; (Col 25 Lines 21-26 “The AI models assesses whether the data meets complex quality and compliance metrics and flag high-risk data accordingly and generate above-mentioned insights. The AI model by performing all these insights empower the user (e.g. a reviewer, a regulatory analyst, etc.) to quickly pinpoint and take corrective actions.”
Col 38 Lines 10-17 “The visualization toolbox enables the user to create the data visualization. The user upon dragging, dropping and adding the data visualization, provides different charts and metrics and things like that.
Examiner’s Note: Conducting assessment of data for providing insights in order to take corrective actions is read as “solutioning and problem solving landscape to mitigate risk”.
Biswas fails to teach: and determining best approaches to restructuring programme workstreams, team structures, task prioritisation and benefit realisation tracking.
However, Malvankar teaches: and determining best approaches to restructuring programme workstreams, team structures, task prioritisation and benefit realisation tracking. (Col 1 Lines 39-41 “A workload scheduled for an incompatible cloud environment results in the workload failing to execute.” Col 5 Lines 18-25 “The server (110) is shown with an artificial intelligence (AI) platform (150) to support optimizing workload scheduling and provisioning in a distributed shared resource environment. More specifically, the AI platform (150) is configured with one or more tools to leverage unstructured data corresponding to application artifacts related to a workload, and to identify and schedule the workload on a compatible host.
Examiner’s Note: Optimizing workload scheduling and provisioning in a distributed shared resource environment is read as determining best approaches to restructuring program workstreams and team structures. Additionally, the scheduling workloads based on application artifacts and unstructured data is read as task prioritization and benefit realization tracking as doing so leads to the obvious benefit of accounting for any intermediary specifications or requirements and avoiding workload failure.
Biswas and Malvankar are considered to be analogous to each other as they are all in the field of machine learning. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the method for organizing big-data and extracting workstream parameters taught by Biswas with the determining of best approaches to restructuring workstreams and task prioritization taught by Malvankar in order to improve productivity and reduce wasted resources in scheduled workloads. (Col 4 Lines 42-46 “Failed workloads directly impact productivity and waste resources for users scheduling the workloads. Accordingly, application of a configuration with respect to scheduling a workload should optimally account for availability in conjunction with compatibility.”)
Regarding Claim 2:
Biswas teaches: A computer based method for organising big-data and extracting workstream parameters to expedite digital transformations, which method employs analytics solution that predicts and mitigates digital programme transformation risks using human-centred interventions around skills, team composition, leadership, communication and collaboration, comprising: (It is noted that these limitations amount to intended use. A recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim.
In addition, when reading the preamble in the context of the entire claim, the recitations are not limiting because the body of the claim describes a complete invention and the language recited solely in the preamble does not provide any distinct definition of any of the claimed invention’s limitations. Thus, the preamble of the claim(s) is not considered a limitation and is of no significance to claim construction. See Pitney Bowes, Inc. v. Hewlett-Packard Co., 182 F.3d 1298, 1305, 51 USPQ2d 1161, 1165 (Fed. Cir. 1999). See MPEP § 2111.02.
providing a cloud based hosting and analytical Al platform; (Col 2 Lines 65-67 “The present disclosure discloses one or more aspects of data-mining and AI workflow platform for structured and unstructured data.”
Col 29 Lines 45-49 “The core web service 104 is configured to share the AI model as at least one of a web service, and an application to a client of the user. The core web service 104 shares the AI model by deploying the AI model on a cloud server and/or a physical server.”)
connecting users and users operating programs via secure means to the platform; (Col 26 Lines 23-30 “The system comprises a server, a device 102, and a database. The device is associated with a user. The server may communicate with the device 102 through a wired connection and/or a wireless connection. The user interacts with the server through a web service and/or an application accessed via the device 102. The server provides a user interface to the user through at least one of the web services, and/or the application.”
Examiner’s Note: Database is read as knowledge system. The server, read as the platform, is connected to users through user devices and the user interface/applications/web services that are ran on those user devices.)
authenticating users and workstreams; parsing structured and unstructured data from the users operating programs; (Col 26 Lines 39-44; Lines 52-57 “The authentication service authenticates and authorizes the user to interact with the server upon providing user credentials. The core web service 104 allows the user to interact with the server upon the authentication and the authorization of the user is enabled by the user. The core web service 104 receive data from a data source.” Examiner’s Note: The user’s interactions and input are read as workstream.)
parsing structured and unstructured data from the users operating programs; (Col 3 Lines 1-15 “The method comprises receiving data from a data source, wherein the data comprises at least one data format of at least one of structured data, semi-structured data and unstructured data; indexing and analyzing the received data; scheduling and uploading automatically the data to a database as per the indexing; visualizing the data and determining at least one of the structured data, the semi-structured data, and the unstructured data from the data uploaded; cleansing and filtering the data based on at least one of an input from a user, and a predefined rule;”
Examiner’s Note: The data that is structured, semi-structured, and unstructured comes from user input, which is read as users operating programs. The cleansing, filtering, indexing, and analysis of the data is read as parsing.)
extracting from the users operating programs sequence, volume and intensity of tasks to determine optimum fulfilment profiles; (Col 24 Lines 59-67, Col 25 Lines 1-6 “Under the dashboard and visualization service, the server provides data data preparation and visualizations under which data visualizations play a key role in preparing the data for machine learning. Data visualizations service/tool is configured to investigate and analyze the data for valuable insights, enabling your analyses to be more targeted and precise. The data visualizations tool empowers the user with a stronger data-intuition, which will help the user to identify the right AI model. The dashboard and visualization tools further perform passive data monitoring and exploration. The server passively monitors the data and generates a more holistic view of the user's network and informs the user of potential issues that may be directly affecting.”
Col 25 Lines 21-26 “The AI models assesses whether the data meets complex quality and compliance metrics and flag high-risk data accordingly and generate above-mentioned insights. The AI model by performing all these insights empower the user (e.g. a reviewer, a regulatory analyst, etc.) to quickly pinpoint and take corrective actions.”
Examiner’s Note: The data being received from user input is read as users operating programs. The investigation and analysis of the data though visualization is read as extraction. Generating a holistic view of the user’s network and informing of potential issues is read as extracting sequence, volume and intensity of the challenges/problems. Taking corrective actions is determining the optimum solution profiles.)
performing analytics to prescribe risk mitigating actions and determining risk profile; (Col 24 Lines 59-67, Col 25 Lines 1-6 “Under the dashboard and visualization service, the server provides data data preparation and visualizations under which data visualizations play a key role in preparing the data for machine learning. Data visualizations service/tool is configured to investigate and analyze the data for valuable insights, enabling your analyses to be more targeted and precise. The data visualizations tool empowers the user with a stronger data-intuition, which will help the user to identify the right AI model. The dashboard and visualization tools further perform passive data monitoring and exploration. The server passively monitors the data and generates a more holistic view of the user's network and informs the user of potential issues that may be directly affecting.”
Examiner’s Note: The server analyzing data and providing data visualizations to help the user identify the right AI model is read as performing analytics to prompt, promote, and prescribe solutioning. Informing the user of potential issues is read as risk mitigating action. Combining that with generating a holistic view of the user’s network is read as determining a risk profile.)
clustering and sequencing tasks relative to defined risk profile; Col 26 Lines 1-11 “The AI-enhanced RPA in the Real World finds application in port and border security. While scanning nowadays can be automated with a little programming, that data must still be manually processed and assessed for risk. The AI model, however, this overwhelming task becomes infinitely quicker, intuitive, and powerfully predictive. Rather than manually reviewing 26,000 container scans per day one-by-one, the AI-integrated RPA solution can be customized to generate a report indicating which of the 26,000 containers are likely to be high, medium, and low risk. The time previously spent by CBP staff and agents to conduct this same risk assessment can now be converted into time conducting investigations.”
Examiner’s Note: In this example, the defined risk profile is maintaining port and border security. Port and border security are read as previously successful solution attempts from across the community. Furthermore, the AI automates labeling the risk severity level for each container, AKA clustering tasks to be solved, with each container being a potential investigation for staff to operate on. The containers are effectively clustered and sequenced based on their high, medium, or low risk level based on border security principles and consequences of investigative failure.
predicting gaps and flagging emerging risks continuously during the lifecycle of the programs; (Col 35 Lines 44-51 “The image shown in FIG. 3j is an exemplary screenshot of the at least one future pattern predicted by the AI model. For an instance, when data (up to year 2006) corresponding to a graph is provided as an input to the AI model, the AI model analyzes the at least one pattern associated with the data and predicts the at least one future pattern 318 for upcoming years (i.e. after 2006) based on the data. The at least one data pattern may depict at least one of risks and trends from the data.”)
compiling and presenting metric and graphical representations to mitigate risk; (Col 25 Lines 21-26 “The AI models assesses whether the data meets complex quality and compliance metrics and flag high-risk data accordingly and generate above-mentioned insights. The AI model by performing all these insights empower the user (e.g. a reviewer, a regulatory analyst, etc.) to quickly pinpoint and take corrective actions.”
Col 38 Lines 10-17 “The visualization toolbox enables the user to create the data visualization. The user upon dragging, dropping and adding the data visualization, provides different charts and metrics and things like that.”
Examiner’s Note: Conducting assessment of data for providing insights in order to take corrective actions is read as “solutioning and problem solving landscape to mitigate risk”.
Biswas fails to teach: and presenting restructured workstreams.
However, Malvankar teaches: and presenting restructured workstreams. (Col 5 Lines 18-25 “The server (110) is shown with an artificial intelligence (AI) platform (150) to support optimizing workload scheduling and provisioning in a distributed shared resource environment.
Col 17 Lines 47-61 “In one example, management layer (730) may provide the following functions: resource provisioning, metering and pricing, user portal, service layer management, and SLA planning and fulfillment… User portal provides access to the cloud computing environment for consumers and system administrators.”
Examiner’s Note: Optimizing workload scheduling and provisioning in a distributed shared resource environment is read as determining best approaches to restructuring program workstreams and team structures. Additionally, the user portal presents the results of the distributed shared resource environment to the user.)
Biswas and Malvankar are considered to be analogous to each other as they are both in the field of machine learning. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the method for organizing big-data and extracting workstream parameters taught by Biswas with the presenting of restructured workstreams taught by Malvankar in order to optimize resources of the service and provide transparency for users of the service. (Col 16 Lines 3-9 “Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some layer of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported providing transparency for both the provider and consumer of the utilized service.”)
Regarding Claim 3:
Biswas further teaches: A method according to claim 1, wherein the data offered is in the form of documents, text, images, video, media files, metadata etc. (Col 3 Lines 1-4 “In an aspect, a method is described. The method comprises receiving data from a data source, wherein the data comprises at least one data format of at least one of structured data, semi-structured data and unstructured data;”
Col 14 Lines 20-27 “As used herein, “Unstructured data” (or unstructured information) is information that either does not have a pre-defined data model or is not organized in a pre-defined manner. Examples of “unstructured data” comprises, health records, audio, video, analog data, images, files, books, journals, documents, meta data, geo-spatial data, and unstructured text such as the body of an e mail message, Web page, or word-processor document, etc.”)
Regarding Claim 4:
Biswas further teaches: A method according to claim 3, wherein the parsing of the data extracts metadata such as subject matter, topics, elements; (Col 35 Lines 31-37 “The AI model also discovers topics from the documents/literatures and discovers hidden semantic structures in a text body of the literature/document and intuitively understand the different topics covered in the given document/literature.”
Examiner’s Note: The system understands semantic structures of texts and different topics which is read as subject matter, topics and elements. It also tags and classifies images.)
and tags with keywords, location and codes of inter-connections. (Col 22 Lines 64-67 & Col 23 Lines 2-9 “The system is configured to perform a real time trend analysis and gain valuable insights on emerging trends by studying causes and catalysts from social media... The system is configured to perform image search and insights through at least searching, tagging and classifying the images. The system is configured to recognize patterns from images on the fly.
Col 23 Lines 10-24 “The system through the AI model perform location analysis and track real-time location patterns from the social data. The system enables the user to use customizable heat maps (i.e., activity clusters) to monitor and analyze complex data relationships.
Examiner’s Note: The system taught by Biswas parses through multiple types of data from documents to images to location data from social media. Also, it performs location analysis and uses heat maps to monitor and analyze complex data relationships which is read as “codes of inter-connections”.
Regarding Claim 5:
Biswas further teaches: A method according to claim 4, wherein the clustering is arranged with filters offered from the metadata and selectable by a user. (Col 21 Lines 35-48 “The data pipelines provide real-time data streams… The system enables to easily hook the real-time data streams, message queues, or real-time API endpoints through an ingestion unit and securely index the data. The data pipelines also provide data lake and data marts that index the metadata that lets to easily visualize, analyze, and create the AI models with high efficiency... The data pipelines offer a graphical user interfaces to schedule the data extraction automatically from third-party databases... The data pipelines provide a graphical interface lets the user to parse, filter, and enrich data on-the-fly during ingestion.”
Examiner’s Note: Biswas teaches a graphical interface that allows the user to parse and filter the incoming data that is highly organized (databases/data lake/data mart). Additionally, the data is indexed according to metadata. This is read as “clustering” which is “arranged with filters offered from the metadata” and “selectable by a user”.)
Regarding Claim 6:
Biswas fails to teach: A method according to claim 1, further comparing and quantifying a value of compatibility of said data or data subsections with the users' operating program sequence.
However, Malvankar further teaches: A method according to claim 1, further comparing and quantifying a value of compatibility of said data or data subsections with the users' operating program sequence. (Col 7 Lines 38-48 “The scheduling manager (158) is configured to selectively schedule the workload responsive to the initial, or the preliminary, host identification by the director (156). The scheduling manager (158) assesses the compatibility of the workload with the identified one or more hosts, and responsive to a determination of compatibility schedules the workload on the identified host. In an exemplary embodiment, a compatibility matrix is identified in the crowdsourced data. The compatibility matrix conveys requirements to support the workload, with the requirements includes elements such as hardware, software, and libraries.”
Col 14 Lines 50-57 “Program/utility (540), having a set (at least one) of program modules (542), may be stored in memory (506) by way of example, and not limitation, as well as an operating system, one or more application programs, other program modules, and program data… Program modules (542) generally carry out the functions and/or methodologies of embodiments to optimize workload scheduling and provisioning in a distributed shared resource environment.”
Examiner’s Note: Malvankar teaches that a scheduling manager assesses the compatibility of a workload with a potential host, which is a device that can complete the workload. This compatibility, AKA value of compatibility, is based on data, hardware, software, libraries, operating system, and other requirements. This is read as “data or data subsections with the users' operating program sequence.”)
Biswas and Malvankar are considered to be analogous to each other as they are all in the field of machine learning. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the method for organizing big-data and extracting workstream parameters taught by Biswas with the quantifying the compatibility between data and user operating programs taught by Malvankar in order to avoid assigning incompatible devices which will resulting in execution failure, especially considering the cloud environment. (Col 4 Lines 26-32 “However, software releases for different hardware architecture can often make the drivers, e.g. software component(s) that allows communication between the operating system and a corresponding hardware device, and toolkit software incompatible with the cloud environment. A workload scheduled for an incompatible cloud environment results in the workload failing to execute.”)
Regarding Claim 7:
Biswas further teaches: A method according to claim 6, wherein the parsing comprises data from two or more sources. (Col 21 Lines 42-44 “The data pipelines offer a graphical user interfaces to schedule the data extraction automatically from third-party databases.”
Examiner’s Note: Databases is plural which is read to be two or more sources of data.)
Regarding Claim 8:
Biswas further teaches: A method according to claim 1, wherein the presenting comprises a combination of high quantifying a value of subsections from multiples data sources. (Col 21 Lines 42-44 “The data pipelines offer a graphical user interfaces to schedule the data extraction automatically from third-party databases… The data pipelines provide a graphical interface lets the user to parse, filter, and enrich data on-the-fly during ingestion.”
Examiner’s Note: Databases is plural which implies two or more data sources. Extraction, parsing, filtering, and enriching of data is read as “high quantifying a value of subsections” under BRI given that the Specification does not explicitly clarify the term “high quantifying”.
Regarding Claim 10:
Biswas further teaches: A system for organising big-data and extracting workstream parameters to expedite digital transformations, (Col 2 Lines 65-67 “The present disclosure discloses one or more aspects of data-mining and AI workflow platform for structured and unstructured data.” Col 3 Lines 1-15 “The method comprises receiving data from a data source, wherein the data comprises at least one data format of at least one of structured data, semi-structured data and unstructured data; indexing and analyzing the received data;”
Examiner’ Note: Handling data sources with structured, semi-structured, and unstructured data that involves data mining and data visualization due to size is read as organizing big-data in an expedited way.
Col 3 Lines 1-15 “scheduling and uploading automatically the data to a database as per the indexing; visualizing the data and determining at least one of the structured data, the semi-structured data, and the unstructured data from the data uploaded; cleansing and filtering the data based on at least one of an input from a user, and a predefined rule; labeling and annotating seamlessly the data available in the database; and building an artificial intelligence (AI) model based on at least one of the data available in the database, the input from the user, and the predefined rule.”)
Examiner’ Note: Indexing, analyzing, filtering, and labeling the data in order to build an AI model is read as extracting workstream parameters.
which method employs analytics solution that predicts and mitigates digital programme transformation risks using human-centred interventions around skills, team composition, leadership, communication and collaboration, performing the method of claim 1. (Col 25 Lines 19-26 “The AI models assesses whether the data meets complex quality and compliance metrics and flag high-risk data accordingly and generate above-mentioned insights. The AI model by performing all these insights empower the user (e.g. a reviewer, a regulatory analyst, etc.) to quickly pinpoint and take corrective actions.”
Examiner’ Note: Biswas teaches models that use analytics to make predictions and mitigate risks by providing metrics and flagging risks. Furthermore, the models provide high-risk indicators to the necessary agent (reviewer or regulatory analyst) to encourage corrective actions which can be read as mitigating digital program transformation risks using human-centered interventions around skills, team composition, leadership, communication and collaboration. Ultimately, the AI models compute metrics and risk data in order to provide insights to inform the user with human-centered faculties of the best course of action.)
Regarding Claim 11:
Biswas further teaches: A non-transitory computer-readable storage medium having stored thereon computer-readable code, which, when executed by computing apparatus, causes the computing apparatus to perform the method of claim 1. (Col 5 Lines 17-20 “In another aspect, a non-transitory computer storage medium storing a sequence of instructions is described. The sequence of instructions which when executed by a processor, causes…”)
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Biswas in view of Malvankar in further view of Guan et al. (US 20210042767 A1), hereinafter referred to as Guan.
Regarding Claim 9:
Biswas and Malvankar fail to teach: wherein the method is for use in virtual environments, in online virtual worlds or metaverse.
However, Guan teaches: A method according to claim 1, wherein the method is for use in virtual environments, in online virtual worlds or metaverse. ([0035] “The web feeds 104 may be yet another source of data. Data received at the web feeds 104 may include data from various web sources, such as websites, social media, syndication, aggregators, or from scraping… Data from social media may also include any type of internet-based application built upon creation and exchange of user-generated content, which may include information collected from social networking, microblogging, photosharing, news aggregation, video sharing, livecasting, virtual worlds, social gaming, social search, instant messaging, or other interactive media sources.”)
Biswas and Guan are considered to be analogous to each other as they are all in the field of machine learning. Therefore, before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art to combine the method for organizing big-data and extracting workstream parameters taught by Biswas with the use within virtual worlds or metaverse taught by Guan in order to incorporate human-created content from social networks and provide real-time data exchange from online platforms for better predictive modeling that simulates human activity. ([0031] “The digital content management system 100 may operate in a network or an enterprise environment where data is exchanged, and where products or services are being offered to customers. More specifically, the digital content management system 100 may provide real-time or near real-time monitoring and analysis of data exchange and data storage, as well as an artificial intelligence system that uses analytics and predictive modeling to manage leads and the creation of a lead analytical record.”
[0035] “This may also include RSS feeds, which allow users to access updates to online content… Other forms of scraping may also include document object model (DOM) parsing, computer vision, and natural language processing (NLP) to simulate human browsing to enable gathering web page content for offline parsing.”)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSHUA Y JOO whose telephone number is (571) 272-5466. The examiner can normally be reached M-Th 9:30am-7:30pm.
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/JOSHUA Y JOO/ Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/ Supervisory Patent Examiner, Art Unit 2128
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