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 .
This action is in response to the application filed 8 January 2025. Claims 1-20 are pending and have been examined.
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 16-20 is directed towards a computer readable medium. It is construed that these components lacks physical structure and is not statutory because it is not a process, machine, manufacture, or composition of matter. “It must be clear in the claim language that the computer readable medium is "non-transitory" and currently it is not clear” or “It is construed that these components lacks physical structure and can encompass signals per se”. This is not statutory because it is not a process, machine, manufacture, or composition of matter. Thus claim 16-20 is non-statutory and therefore rejected.
The United States Patent and Trademark Office (USPTO) is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO. See In re Zletz, 893 F.2d 3 19 (Fed. Cir. 1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. See MPEP 21 1 1.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 U.S.C. @ 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Cir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility Under 35 U.S.C. j 101, Aug. 24, 2009; p. 2.
Appropriate correction is required.
Additionally, claims 1–20 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite abstract ideas falling within the groupings of (1) mathematical concepts specifically, mathematical calculations and mathematical relationships and (2) certain methods of organizing human activity specifically, fundamental economic principles or practices (assessing and predicting corporate performance) and commercial or legal interactions (business analytics and benchmarking). These judicial exceptions are not integrated into a practical application because the additional elements in the claims, namely generic databases, generic processors, and a generic report-generation and transmission step, merely implement the abstract idea using generic computer components performing generic computer functions without improving the computer or any other technology. The claims do not include additional elements sufficient to amount to significantly more than the judicial exception because each additional element, considered individually and in combination, constitutes well-understood, routine, and conventional computer activity and/or insignificant extra-solution activity.
Step 1
Regarding Step 1 of the Subject Matter Eligibility Test for Products and Processes, claims 1–14 are directed to a system/machine, claim 15 is directed to a method/process, and claims 16–20 are directed to a non-transitory computer-readable medium/manufacture. Therefore, all claims fall within the statutory categories of invention.
Step 2A Prong 1
The independent claims 1, 15, and 16 each recite an abstract idea. Specifically, the independent claims recite the following limitations that constitute judicial exceptions:
Chronologically sorting a respective employment history for each of the individuals based on social media data;
Generating, for periods-of-time, metric values for metrics of the users based on the social media data;
Identifying a reference company, a metric-of-interest, and a period-of-interest for assessment of the reference company;
For each of the periods-of-time within the period-of-interest, generating a respective company index score for each of the companies based on a percentile rank of the respective metric value for the metric-of-interest relative to a respective set of benchmark companies;
Identifying which of the individuals are associated with the reference company based on the respective employment histories;
For each of the individuals associated with the reference company, calculating a respective individual index score based on the respective company index scores of the reference company and preceding company index scores of preceding companies-of-employment; and
For the reference company, calculating a collective index score based on the individual index scores of the individuals associated with the reference company.
Abstract Idea Grouping Analysis
Option A – Mathematical Concepts (MPEP § 2106.04(a)(2), Subsection I):
The foregoing limitations recite mathematical calculations and mathematical relationships as enumerated in MPEP § 2106.04(a)(2), subsection I. Specifically, the step of generating a “company index score based on a percentile rank of the respective metric value for the metric-of-interest relative to a respective set of benchmark companies” (claims 1, 15, 16) explicitly recites a mathematical calculation — computing a percentile rank to produce a numerical score. Dependent claims 8–9 and 17 further confirm this characterization by reciting that the processors determine a mean and a standard deviation of benchmark company metric values, calculate a z-score by dividing the difference between the reference company’s metric value and the benchmark mean by the standard deviation, and convert the z-score to a percentile rank all classical mathematical operations. See SAP America, Inc. v. InvestPic, LLC, (claims to “a series of mathematical calculations based on selected information” are directed to abstract ideas). Dependent claims 10 and 18 alternatively recite assigning companies to numerical “buckets” and determining a percentile rank from the bucket distribution also a mathematical calculation. The step of calculating an individual index score based on the company index scores of the reference company and preceding company index scores of preceding companies-of-employment recites a mathematical relationship, specifically a running geometric mean as described in the specification at [0145]–[0146] and recited explicitly in dependent claims 11–12 and 19. The step of calculating a collective index score based on individual index scores recites computing a statistical mean as confirmed by the specification at [0149] and dependent claims 13 and 20. Together, these limitations recite multiple mathematical calculations and mathematical relationships, each of which squarely falls within the mathematical concepts grouping of abstract ideas.
Option B – Certain Methods of Organizing Human Activity (MPEP § 2106.04(a)(2), Subsection II):
The claims, viewed as a whole, additionally recite a fundamental economic principle or practice and a commercial interaction. The stated purpose of claims 1, 15, and 16 is “assessing and predicting corporate performance.” Generating index scores to assess a company’s performance relative to peer/benchmark companies and predicting its future performance is a quintessential fundamental economic and commercial activity one that investors, analysts, and business decision-makers routinely perform for investment decisions, competitive analysis, and business strategy. See Spec. at [0003]–[0005] (describing the background as companies and investors comparing performance metrics to decide where to invest and how to position themselves). The claimed operations of collecting employment histories, identifying company-associated individuals, and computing weighted benchmark scores to assess corporate performance constitute commercial business intelligence activities that fall within the “fundamental economic principles or practices” and “commercial or legal interactions” sub-groupings of certain methods of organizing human activity. See Alice Corp. Pty. Ltd. v. CLS Bank Int’l; OIP Techs., Inc. v. Amazon.com, Inc., (price optimization based on collected data is a fundamental economic concept). See MPEP § 2106.04(a)(2), subsection II.
Step 2A Prong One Conclusion
The independent claims 1, 15, and 16, and all claims dependent thereon, recite an abstract idea within the mathematical concepts grouping and the certain methods of organizing human activity grouping. The analysis proceeds to Step 2A Prong Two.
Step 2A Prong Two
Identification of Additional Elements
The claims recite the following additional elements beyond the identified abstract idea:
“One or more databases configured to store social media data of user profiles of users on a social media platform” (claims 1, 15, 16);
“One or more processors” configured to execute the claimed operations (claims 1, 15, 16);
A “social media platform” as the source from which social media data is collected (claims 1, 15, 16); and
“Generate and transmit a report indicative of corporate performance of the reference company” (claims 1, 15, 16).
Analysis of Additional Elements
Improvement to Technology or Technical Field (MPEP § 2106.05(a)):
The claims do not recite an improvement to the functioning of a computer or to any other technology or technical field. The specification consistently describes the one or more processors 110, the memory 120, and the databases 150–190 as generic computing components. See Spec. at [0058]–[0065] (the benchmarking system 100 includes “one or more processors,” “memory,” “one or more input devices,” “one or more output devices,” and separate databases, each described in terms of generic, standard computing functionality). The specification at [0059] expressly describes the processors as “any suitable processing device or set of processing devices such as, but not limited to, a microprocessor, a microcontroller-based platform, an integrated circuit, etc.” confirming their generic nature. The claimed operations of collecting social media data, sorting employment records, computing percentile-rank index scores, and generating a report are performed using computers as tools to carry out the abstract idea, rather than improving computer functionality itself. The specification at [0026] frames the invention as systems that “automatically generate index scores of companies and/or individuals, based on social media content” to assess performance confirming the computer serves as an instrument to perform an analytical and economic task. No new or improved computer architecture, data structure, or technical mechanism is described or claimed. See Alice Corp., (rejecting eligibility where claims “simply instruct the practitioner to implement the abstract idea… on a generic computer”); TLI Communications LLC v. AV Automotive LLC, (“mere recitation of concrete, tangible components is insufficient to confer patent eligibility to an otherwise abstract idea”). See MPEP § 2106.05(a).
Particular Machine (MPEP § 2106.05(b)):
The claims do not recite use of a particular machine that imposes meaningful limits on the claim. The recited “one or more databases,” “one or more processors,” and “social media platform” are generic computing components that do not constrain the claim in any meaningful way. Any general-purpose computing system with standard storage and processing capabilities could implement the claimed functions. The claims are not limited to any particular architecture, arrangement, or configuration of these components. The social media platform merely specifies the source of the input data, which is a field-of-use limitation that does not render the claims eligible. See MPEP § 2106.05(b); Alice Corp.
Mere Instructions to Apply the Exception (MPEP § 2106.05(f)):
The additional elements amount to no more than mere instructions to apply the abstract idea using a generic computer. The claims recite generic databases performing generic data-storage functions, generic processors performing mathematical computations and analytical evaluations, and a generic report-generation and transmission step. This is tantamount to adding the instruction “apply it” to the abstract idea i.e., compute and evaluate corporate performance indexes using a computer. See Alice Corp., (“Simply implementing a mathematical principle on a physical machine, namely a computer, is not a patentable application of that principle”); Bancorp Servs., LLC v. Sun Life Assurance Co. of Canada (U.S.), (ineligible where computer was used to “facilitate” implementation of a mathematical/economic concept). See MPEP § 2106.05(f).
Insignificant Extra-Solution Activity (MPEP § 2106.05(g)):
The additional element of “storing social media data in one or more databases” constitutes insignificant pre-solution data-gathering activity. Collecting raw input data from a social media platform is incidental and ancillary to the core abstract process of computing index scores and is not integrated into the abstract idea in any meaningful way. Similarly, the element of “generating and transmitting a report indicative of corporate performance” constitutes insignificant post-solution activity transmitting the output results of the abstract idea is a standard data-output step that does not integrate the abstract idea into a practical application. These data-gathering and data-output steps are mere bookends to the abstract idea and do not confer eligibility. See MPEP § 2106.05(g); Ultramercial, Inc. v. Hulu, LLC.
Considering the additional elements individually and in combination, the claim as a whole does not integrate the judicial exception into a practical application. The recited generic databases and generic processors perform only generic computing functions. The social media platform merely identifies the source of input data. The report generation and transmission step is mere post-solution output. None of these elements, individually or in combination, impose any meaningful limits on the practice of the abstract idea, improve the functioning of a computer or other technology, or otherwise integrate the abstract idea into a practical application. Accordingly, the claims are directed to an abstract idea.
STEP 2B
As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than mere instructions to apply the abstract idea using generic computer components performing generic computing functions. The same analysis applies in Step 2B mere instructions to apply an exception using generic computer components cannot provide an inventive concept. See MPEP § 2106.05(f); Alice Corp.
Well-Understood, Routine, Conventional Activity Analysis
The additional elements, when considered individually and in combination, are well-understood, routine, and conventional activities in the field. Specifically:
“One or more databases configured to store social media data” — The courts have recognized storing and retrieving data in databases as well-understood, routine, and conventional computer activity. See MPEP § 2106.05(d)(II), citing Versata Dev. Group, Inc. v. SAP Am., Inc.; OIP Techs., Inc. v. Amazon.com, Inc. The specification at [0058]-[0065] confirms that the recited databases (social media databases 150, date database 160, metric database 170, benchmark database 180, and index score database 190) are standard database structures with no structural novelty beyond their function of storing particular categories of data.
“One or more processors” configured to execute the claimed computational operations — The courts have recognized the use of generic processors to perform computational operations as well-understood, routine, and conventional activity. See MPEP § 2106.05(d)(II), citing Alice Corp., 573 U.S. at 225–26; TLI Communications. The specification at [0059] expressly confirms the processors are generic and interchangeable with any suitable processing device.
“Generating and transmitting a report” — The courts have recognized generating and transmitting data outputs over a network as well-understood, routine, and conventional activity. See MPEP § 2106.05(d)(II), citing TLI Communications; OIP Techs. The specification at [0150] confirms the report is generated and transmitted using generic communication means such as email, text message, link, or notification.
Taken individually, none of these additional elements amounts to significantly more than the judicial exception. Taken in combination, the elements recite a generic computing system (databases + processors + output) performing the standard functions of storing input data, executing mathematical calculations, and transmitting results a combination that is entirely routine and conventional in the data analytics field. The fact that the inputs are drawn from social media data and the outputs are corporate performance index scores does not transform the generic computing infrastructure into something more. Applicant may argue that the claimed methodology is unconventional, citing the specification’s characterization at [0031] of the invention as including “an unconventional and specific set of rules to generate new data based on social media content.” However, characterizations in the specification of the abstract idea itself as novel or unconventional cannot supply the “significantly more” required under Alice/Mayo Step 2B. The inventive concept must reside in the additional elements beyond the abstract idea, not in the abstract idea itself. See Mayo Collaborative Servs. v. Prometheus Labs., Inc., (“The ‘novelty’ of any particular [abstract idea] does not determine whether the patent claim is novel and nonobvious”); Genetic Techs. Ltd. v. Merial LLC) (“An inventive concept cannot be furnished by the unpatentable…abstract idea itself”).
Considering the additional elements individually and in combination, the claims do not include additional elements sufficient to amount to significantly more than the abstract idea. The claims are not patent eligible.
Dependent Claims Analysis
The dependent claims do not add limitations that integrate the judicial exception into a practical application or provide an inventive concept:
Claims 2–3: These claims further define the periods-of-time and period-of-interest as predefined equal-duration time windows, and add the step of converting employment start and end dates into a longitudinal date profile. These limitations further specify the parameters and data-formatting operations for the abstract mathematical calculations but do not transform those computations into a practical application. Converting date ranges into a longitudinal date profile is a mathematical data-reformatting operation that itself constitutes a mathematical concept, or is at most insignificant extra-solution data organization ancillary to the abstract idea. These limitations do not impose any meaningful limits that would integrate the exception into a practical application.
Claims 4–5: These claims specify categories of individual metrics (career-tenure, company-tenure, role-tenure, experience-tenure, and industry-tenure) and company metrics (headcount, time-interval, diversity, gender-equity, and company-tenure). These limitations merely narrow the type of input data subject to the abstract mathematical calculations. Specifying the domain or category of data used as inputs constitutes a field-of-use limitation and does not supply an inventive concept. See MPEP § 2106.05(h).
Claims 6–7: These claims add the process of identifying benchmark companies for the reference company by identifying the reference company’s industry classification, identifying headcount for each period-of-time, selecting peer companies based on headcount and industry classification, and selecting benchmark companies from the peer companies. These limitations describe additional steps of the abstract process of identifying comparable companies a mental or analytical evaluation and do not add any non-abstract element that integrates the exception into a practical application or provides significantly more.
Claims 8–9 and 17: These claims specify one method of computing the company index score: determining the mean and standard deviation of the benchmark companies’ metric values, computing a z-score by dividing the difference between the reference company’s metric value and the benchmark mean by the benchmark standard deviation, and converting the z-score to a percentile rank. These are explicit mathematical operations that reside within the abstract idea itself. They add no additional elements beyond the abstract idea.
Claims 10 and 18: These claims specify an alternative method of computing the company index score via bucket assignment and percentile-rank determination based on bucket distribution. This is likewise an explicit mathematical calculation within the abstract idea and does not integrate the exception into a practical application.
Claims 11–12 and 19: These claims specify that the individual index score is calculated as a running geometric mean (with optional time decay) of the relevant company index scores throughout the individual’s employment history. This is an additional mathematical calculation that itself falls within the mathematical concepts grouping of abstract ideas and adds nothing beyond the abstract idea.
Claims 13 and 20: These claims specify that the collective index score is calculated as a mean of the individual index scores for the individuals associated with the reference company. This is an additional mathematical calculation within the abstract idea.
Claim 14: This claim specifies that the processors are configured to identify the preceding companies-of-employment from the chronologically sorted employment history. This limitation describes a data-lookup step that is incidental to the abstract analytical process and constitutes insignificant extra-solution activity that does not integrate the exception into a practical application.
For the foregoing reasons, claims 1–20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter. The claims are directed to the abstract ideas of: (1) performing mathematical calculations to generate numerical index scores (percentile ranks, z-scores, running geometric means, and statistical means) representing a company’s performance relative to benchmark companies; and (2) assessing and predicting corporate performance, which is a fundamental economic practice and commercial interaction. The additional elements in the claims generic databases, generic processors, a generic social media data source, and a generic report-generation and transmission step amount to no more than implementing the abstract idea on a generic computer using well-understood, routine, and conventional components and operations. This is insufficient to integrate the abstract idea into a practical application or to provide an inventive concept. See Alice Corp. Pty. Ltd. v. CLS Bank Int’l.
Applicant is invited to respond to this rejection by amending the claims or presenting arguments for reconsideration. Applicant is cautioned that arguments asserting the novelty or unconventionality of the claimed index score calculation methodology or corporate performance assessment process will not overcome this rejection, as the novelty of the abstract idea itself does not confer patent eligibility under § 101. See Mayo; Synopsys, Inc. v. Mentor Graphics Corp., (“a new abstract idea is still an abstract idea”). To overcome this rejection, Applicant should identify specific additional elements in the claims beyond the recited abstract idea — that either: (a) improve the functioning of a computer or other technology or technical field; (b) apply the abstract idea using a particular machine that imposes meaningful limits on the claim scope; or (c) otherwise integrate the abstract idea into a practical application in a manner not addressed above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent may not be obtained through the invention is not identically disclosed or described as set forth in section 102, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 2, 6, and 14-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raikula et al. (U.S. Patent Publication 2015/0254291 A1) (hereafter Raikula) in view of Hardtke et al. (U.S. Patent Publication 2014/0122355 A1) (hereafter Hardtke).
Referring to Claim 1, Raikula teaches a system for assessing and predicting corporate performance based on social media content, the system comprising:
one or more databases configured to (see; par. [0025] of Raikula teaches a database used to store information regarding social media health of a company).
store social media data of user profiles of users on a social media platform, wherein the users include individuals and companies (see; par. [0039] of Raikula teaches profile data of users including job related data, and par. [0025] database stores data associated with the generation of the index, par. [0028]: data received from Facebook, LinkedIn, and Twitter... associated with companies. par. [0049]: social network details database that receives and stores data associated with the identity of... individual social media user, including… job title, career path. par. [0051] individual characteristics database stores data associated with the individual social media user. Together these confirm storage of social media profile data for both individuals and companies).
one or more processors configured to (see; par. [0012] of Raikula teaches a processor, and memory used to aggregate index scores related to social media).
generate, for periods-of-time, metric values for metrics of the users based on the social media data (see; par. [0007] of Raikulu teaches six specific metrics (dimensions): volume, sentiment, velocity, audience size, audience influence, and audience affluence which are derived from social media data these are metric values for metrics of users. par. [0032] states the score can be calculated for a particular time period (e.g., one week, one month, six months, one year)" which teaches the generation of metric values for periods-of-time, par. [0040] further indicates scores stored for specific periods (e.g., weekly, monthly, yearly) confirming temporal, per-period metric generation).
identify a reference company (see; par. [0033] of Raikulu teaches a module that receives the annotated data associated with Fidelity Investments a named, identified reference company is the subject of analysis. Par. [0043] further discloses the module that can compare Fidelity's social media index score, the reference company is specifically identified. Par. [0045] where a research analyst can subscribe to the social media index data feed for a particular company confirming the system identifies and focuses on a specific selected company).
identify a metric-of-interest and a period-of-interest for assessment of the reference company, wherein the period-of-interest includes one or more of the periods-of-time, for each of the periods-of-time within the period-of-interest (see; par. [0007] of Raikulu teaches the names that provide identifiable dimensions/metrics: volume, sentiment, velocity, etc. these are the selectable metrics-of-interest. Par. [0032] provides user-selectable time periods (one week, one month, six months, one year) where these are periods-of-interest. Par. [0045] further the user can supply the system with a customized weighting algorithm, what component signals should be emphasized, what time period(s) should be evaluated, indicating an explicit user identification of metric-of-interest and period-of-interest).
for each of the companies, generate a respective company index score based on a percentile rank of the respective metric value for the metric-of-interest relative to a respective set of benchmark companies (see; par. [0011] of Rikulu teaches the revised index score is compared with an industry benchmark to determine performance of the company with respect to the industry which is viewed to be a company score benchmarked against a set. Par. [0032] teaches the index score is generated per company per period. Par. [0042] and scores compared across entire sector for relative performance. These teach the benchmark-relative scoring framework. Par. [0046] gives an example that there is a rank of a high affluence user which indicates a ranking of the user with respect to the business, par. [0008] and how that is related to the company).
identify which of the individuals are associated with the reference company based on the respective employment histories (see; Abstract of Raikula teaches identifying social media data from a plurality of social media data associated with companies where the data is aggregated (i.e. historical), par. [0046] that takes into account affluence of the individual (i.e. how they are associated with a reference company), par. [0026] using historical data).
for each of the individuals associated with the reference company, calculate a respective individual index score based on the respective company index scores of the reference company and preceding company index scores of preceding companies-of- employment (see; par. [0011] of Rikulu teaches the revised index score is compared with an industry benchmark to determine performance of the company with respect to the industry which is viewed to be a company score benchmarked against a set. Par. [0032] teaches the index score is generated per company per period. Par. [0042] and scores compared across entire sector for relative performance. These teach the benchmark-relative scoring framework. Par. [0054] and comparing the data to previous evaluations (i.e. preceding score)).
for the reference company, calculate a collective index score based on the individual index scores of the individuals associated with the reference company (see; par. [0054] of Raikula teaches the individual weighted leverage score calculation module determines a leverage score associated with the individual user. Par. [0055] the module transmits the affluence and influence adjusted network value score... to the system for incorporation into the process of generating a social media health index for companies. This teaches the exact upward aggregation architecture: individual-level scores company-level index score. The architecture of the limitation is taught; however, the predicate individual scores differ in kind from those claimed.
generate and transmit a report indicative of corporate performance of the reference company (see; pr. [0012] of Raikula teaches the server computing device publishes the generated index score on a data feed for consumption by other computing devices, and transmit step. par. [0041]. The trend data and signal threshold processing module also transmits the index score and trend data to a data publication module where report generated and transmitted. Par. [0045] a research analyst at a mutual fund broker can subscribe to the social media index data feed for a particular company... to use the data in evaluating company performance, confirms the transmitted output is a report indicative of corporate performance used for performance evaluation.
Raikula does not explicitly disclose the following limitation, however,
Hardtke for each of the individuals, chronologically sort a respective employment history (see; Table 1A, pg. 9, 1C pg. 10, Table 1E page 11-1, Table 1F pg. 12 and par. [0009] of Hardtke teaches an example of how a candidates employment history is analyzed based on the history of different activities (i.e. employment history chronologically)).
The Examiner notes that Raikula teaches similar to the instant application teaches generating an index social health. Specifically, Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hardtke teaches identifying candidates for job openings using scoring function based on features in resumes and job descriptions and as it is comparable in certain respects to Raikula which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data. However, Raikula fails to disclose for each of the individuals, chronologically sort a respective employment history.
Hardtke discloses for each of the individuals, chronologically sort a respective employment history.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Raikula the for each of the individuals, chronologically sort a respective employment history as taught by Hardtke since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Raikula, and Hardtke teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 2, see discussion of claim 1 above, while Raikula in view of Hardtke teaches the system above, Raikula further discloses a system having the limitations of:
each of the periods-of-time is a predefined measurement-of-time by which the metrics of the users are assessed, wherein each of the periods-of-time has a same time duration, and wherein the period-of-interest is a time period during which the reference company is compared to the respective set of benchmark companies (see; par. [0032] of Raikula teaches a score calculated for a particular time period (e.g., one week, one month, six months, one year), each period has a single fixed uniform duration. Par. [0040] where a social media index scores for Fidelity Investments, as calculated by the module for specific periods (e.g., weekly, monthly, yearly) and confirms uniform predefined duration periods, furthermore par. [0043] company scores "compared over the same time period the period-of-interest encompasses the comparison window against benchmark companies).
Referring to Claim 6, see discussion of claim 1 above, while Raikula in view of Hardtke teaches the system above, Raikula further discloses a system having the limitations of,
the one or more processors are configured to identify the respective set of benchmark companies for the reference company (see; par. [0011] of Raikula teaches revised index score is compared with an industry benchmark where a benchmark set is identified and used).
Referring to Claim 14, see discussion of claim 1 above, while Raikula in view of Hardtke teaches the system above, Raikula does not explicitly disclose a system having the limitations of, however,
Hardtke teaches for each of the individuals associated with the reference company, the one or more processors are configured to identify the preceding companies-of-employment in the employment history that was chronologically sorted (see; par. [0049] of Hardtke teaches candidates that are looking for a position while most data will be provided by the candidate the company can find additional details on social media including the current and past employers (i.e. employment history) in a list (i.e. order)).
The Examiner notes that Raikula teaches similar to the instant application teaches generating an index social health. Specifically, Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hardtke teaches identifying candidates for job openings using scoring function based on features in resumes and job descriptions and as it is comparable in certain respects to Raikula which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data. However, Raikula fails to disclose for each of the individuals associated with the reference company, the one or more processors are configured to identify the preceding companies-of-employment in the employment history that was chronologically sorted.
Hardtke discloses for each of the individuals associated with the reference company, the one or more processors are configured to identify the preceding companies-of-employment in the employment history that was chronologically sorted.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Raikula for each of the individuals associated with the reference company, the one or more processors are configured to identify the preceding companies-of-employment in the employment history that was chronologically sorted as taught by Hardtke since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Raikula, and Hardtke teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 15, Raikula in view of Hardtke teaches a method for assessing and predicting corporate performance. Claim 15 recites the same or similar limitations as those addressed above in claim 1, Claim 15 is therefore rejected for the same reasons as set forth above in claim 1.
Referring to Claim 16, Raikula in view of Hardtke teaches a computer readable medium. Claim 16 recites the same or similar limitations as those addressed above in claim 1, Claim 16 is therefore rejected for the same reasons as set forth above in claim 1.
Claims 3 and 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raikula et al. (U.S. Patent Publication 2015/0254291 A1) (hereafter Raikula) in view of Hardtke et al. (U.S. Patent Publication 2014/0122355 A1) (hereafter Hardtke) in further view of Lytkin et al. (U.S. Patent Publication 2016/0321362 A1) (hereafter Lytkin).
Referring to Claim 3, see discussion of claim 1 above, while Raikula in view of Hardtke teaches the system above, Raikula in view of Hardtke does not explicitly disclose a system having the limitations of, however,
Lytkin teaches identify a start date and an end date for each employment position in the social media data for the user (see; par. [0012] of Lytkin teaches social medial providing current and past employment data (i.e. position start and end date) which specifically provides member dates of employment), and
convert the start date and the end date into a longitudinal date profile (see; par. [0015] of Lytkin teaches generating a graph that depicts the transition of talent at multiple different companies providing transitional relationships (i.e. dates of employment))
The Examiner notes that Raikula teaches similar to the instant application teaches generating an index social health. Specifically, Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hardtke teaches identifying candidates for job openings using scoring function based on features in resumes and job descriptions and as it is comparable in certain respects to Raikula which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Lytkin teaches determining a company rank utilizing on-line social network data and as it is comparable in certain respects to Raikula and Hardtke which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Raikula and Hardtke discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data. However, Raikula and Hardtke fails to disclose identify a start date and an end date for each employment position in the social media data for the user and convert the start date and the end date into a longitudinal date profile.
Lytkin discloses identify a start date and an end date for each employment position in the social media data for the user and convert the start date and the end date into a longitudinal date profile.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Raikula and Hardtke identify a start date and an end date for each employment position in the social media data for the user and convert the start date and the end date into a longitudinal date profile as taught by Lytkin since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Raikula, Hardtke, and Lytkin teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 4, see discussion of claim 1 above, while Raikula in view of Hardtke teaches the system above, Raikula in view of Hardtke does not explicitly disclose a system having the limitations of, however,
Lytkin teaches the metrics for the individuals include at least one of a career-tenure metric, a company-tenure metric, a role-tenure metric, an experience-tenure metric, or an industry-tenure metric (see; par. [0012] of Lytkin teaches social medial providing current and past employment data (i.e. position start and end date, which is an indicator of tenure, role, and experience) which specifically provides member dates of employment).
The Examiner notes that Raikula teaches similar to the instant application teaches generating an index social health. Specifically, Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hardtke teaches identifying candidates for job openings using scoring function based on features in resumes and job descriptions and as it is comparable in certain respects to Raikula which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Lytkin teaches determining a company rank utilizing on-line social network data and as it is comparable in certain respects to Raikula and Hardtke which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Raikula and Hardtke discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data. However, Raikula and Hardtke fails to disclose the metrics for the individuals include at least one of a career-tenure metric, a company-tenure metric, a role-tenure metric, an experience-tenure metric, or an industry-tenure metric.
Lytkin discloses the metrics for the individuals include at least one of a career-tenure metric, a company-tenure metric, a role-tenure metric, an experience-tenure metric, or an industry-tenure metric.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Raikula and Hardtke the metrics for the individuals include at least one of a career-tenure metric, a company-tenure metric, a role-tenure metric, an experience-tenure metric, or an industry-tenure metric as taught by Lytkin since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Raikula, Hardtke, and Lytkin teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Claims 5 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raikula et al. (U.S. Patent Publication 2015/0254291 A1) (hereafter Raikula) in view of Hardtke et al. (U.S. Patent Publication 2014/0122355 A1) (hereafter Hardtke) in further view of Baig et al. (U.S. Patent Publication 2015/0287051 A1) (hereafter Baig).
Referring to Claim 5, see discussion of claim 1 above, while Raikula in view of Hardtke teaches the system above, Raikula in view of Hardtke does not explicitly disclose a system having the limitations of, however,
Baig teaches the metrics for the companies include at least one of a headcount metric, a time-interval metric, a diversity metric, a gender-equity metric, or a company-tenure metric (see; par. [0045] of Baig teaches employee retention data (i.e. time interval metrics)).
The Examiner notes that Raikula teaches similar to the instant application teaches generating an index social health. Specifically, Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hardtke teaches identifying candidates for job openings using scoring function based on features in resumes and job descriptions and as it is comparable in certain respects to Raikula which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Baig teaches system and method for identifying growing companies and monitoring growth using non-obvious parameters such as social media and as it is comparable in certain respects to Raikula and Hardtke which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Raikula and Hardtke discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data. However, Raikula and Hardtke fails to disclose the metrics for the companies include at least one of a headcount metric, a time-interval metric, a diversity metric, a gender-equity metric, or a company-tenure metric.
Baig discloses the metrics for the companies include at least one of a headcount metric, a time-interval metric, a diversity metric, a gender-equity metric, or a company-tenure metric.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Raikula and Hardtke the metrics for the companies include at least one of a headcount metric, a time-interval metric, a diversity metric, a gender-equity metric, or a company-tenure metric as taught by Baig since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Raikula, Hardtke, and Baig teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 7, see discussion of claim 6 above, while Raikula in view of Hardtke teaches the system above, Raikula further discloses a system having the limitations of,
select the respective set of benchmark companies from the peer companies (see; par. [0011] of Raikula teaches industry (i.e. peer) benchmarking).
Raikula does not explicitly disclose the following limitations, however,
Hardtke teaches select peer companies for the reference company based on the headcount and the industry classification (see; par. [0042] of Hardtke teaches out performing other peer organization, par. [0043] using competitor to competition activity).
The Examiner notes that Raikula teaches similar to the instant application teaches generating an index social health. Specifically, Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hardtke teaches identifying candidates for job openings using scoring function based on features in resumes and job descriptions and as it is comparable in certain respects to Raikula which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data. However, Raikula fails to disclose select peer companies for the reference company based on the headcount and the industry classification.
Hardtke discloses select peer companies for the reference company based on the headcount and the industry classification.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Raikula t select peer companies for the reference company based on the headcount and the industry classification as taught by Hardtke since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Raikula, and Hardtke teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Raikula in view of Hardtke does not explicitly disclose the following limitations, however,
Baig teaches identify an industry classification of the reference company in the social media data; and for each of the periods-of-time within the period-of-interest
(see; par. [0016] of Baig teaches utilizing social media to score and rank each company, par. [0042]-[0043] identifying and extracting company data including social media to provide a general understanding), and
identify a headcount for the reference company in the social media data (see; par. [0044] of Baig teaches using social media, par. [0045] to determine the number of employees at a point in time (i.e. headcount)).
The Examiner notes that Raikula teaches similar to the instant application teaches generating an index social health. Specifically, Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hardtke teaches identifying candidates for job openings using scoring function based on features in resumes and job descriptions and as it is comparable in certain respects to Raikula which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Baig teaches system and method for identifying growing companies and monitoring growth using non-obvious parameters such as social media and as it is comparable in certain respects to Raikula and Hardtke which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Raikula and Hardtke discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data. However, Raikula and Hardtke fails to disclose the metrics for the companies include at least one of a headcount metric, a time-interval metric, a diversity metric, a gender-equity metric, or a company-tenure metric.
Baig discloses the metrics for the companies include at least one of a headcount metric, a time-interval metric, a diversity metric, a gender-equity metric, or a company-tenure metric.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Raikula and Hardtke the metrics for the companies include at least one of a headcount metric, a time-interval metric, a diversity metric, a gender-equity metric, or a company-tenure metric as taught by Baig since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Raikula, Hardtke, and Baig teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Claims 8, 9, 13, 17, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raikula et al. (U.S. Patent Publication 2015/0254291 A1) (hereafter Raikula) in view of Hardtke et al. (U.S. Patent Publication 2014/0122355 A1) (hereafter Hardtke) in further view of Forthman (U.S. Patent Publication 2014/0207478 A1) (hereafter Forthman).
Referring to Claim 8, see discussion of claim 1 above, while Raikula in view of Hardtke teaches the system above, Raikula in view of Hardtke does not explicitly disclose a system having the limitations of, however,
Forthman teaches determine a mean and a standard deviation of the metric values of the metric- of-interest for the respective set of benchmark companies (see; par. [0036] of Forthman teaches an example of determining the mean standard deviation, par. [0049] compared to an original value (i.e. benchmark)), and
calculate a z-score of the reference company for the metric-of-interest based on the metric value of the reference company for the metric-of-interest, the mean of the respective set of benchmark companies, and the standard deviation of the respective set of benchmark companies (see; Abstract, par. [0036]-[0037] of Forthman teaches calculating a z-score based on the mean and standard deviation which provides a metric of a company), and
convert the z-score to the percentile rank for the reference company (see; Abstract of Forthman teaches converting z-score to a percentile value).
The Examiner notes that Raikula teaches similar to the instant application teaches generating an index social health. Specifically, Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hardtke teaches identifying candidates for job openings using scoring function based on features in resumes and job descriptions and as it is comparable in certain respects to Raikula which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Forthman teaches composite scoring and rating methodology and as it is comparable in certain respects to Raikula and Hardtke which generating an index value related to data analysis as well as the instant application it is viewed as analogous art in that it is the analysis of data in a business environment and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Raikula and Hardtke discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data. However, Raikula and Hardtke fails to disclose determine a mean and a standard deviation of the metric values of the metric- of-interest for the respective set of benchmark companies, calculate a z-score of the reference company for the metric-of-interest based on the metric value of the reference company for the metric-of-interest, the mean of the respective set of benchmark companies, and the standard deviation of the respective set of benchmark companies, and convert the z-score to the percentile rank for the reference company.
Forthman discloses determine a mean and a standard deviation of the metric values of the metric- of-interest for the respective set of benchmark companies, calculate a z-score of the reference company for the metric-of-interest based on the metric value of the reference company for the metric-of-interest, the mean of the respective set of benchmark companies, and the standard deviation of the respective set of benchmark companies, and convert the z-score to the percentile rank for the reference company.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Raikula and Hardtke determine a mean and a standard deviation of the metric values of the metric- of-interest for the respective set of benchmark companies, calculate a z-score of the reference company for the metric-of-interest based on the metric value of the reference company for the metric-of-interest, the mean of the respective set of benchmark companies, and the standard deviation of the respective set of benchmark companies, and convert the z-score to the percentile rank for the reference company as taught by Forthman since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Raikula, Hardtke, and Forthman teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 9, see discussion of claim 8 above, while Raikula in view of Hardtke in further view of Forhtman teaches the system above, Raikula in view of Hardtke does not explicitly disclose a system having the limitations of, however,
Forthman teaches to calculate the z-score for the reference company, the one or more processors are configured to divide a difference between the metric value of the reference company and the mean of the respective set of benchmark companies by the standard deviation of the respective set of benchmark companies (see; Abstract and par. [0036]-[0037] of Forthman teaches determining the standard deviation, mean and Z-score, par. [0027] used from the comparison of different entities (i.e. benchmark)).
The Examiner notes that Raikula teaches similar to the instant application teaches generating an index social health. Specifically, Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hardtke teaches identifying candidates for job openings using scoring function based on features in resumes and job descriptions and as it is comparable in certain respects to Raikula which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Forthman teaches composite scoring and rating methodology and as it is comparable in certain respects to Raikula and Hardtke which generating an index value related to data analysis as well as the instant application it is viewed as analogous art in that it is the analysis of data in a business environment and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Raikula and Hardtke discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data. However, Raikula and Hardtke fails to disclose to calculate the z-score for the reference company, the one or more processors are configured to divide a difference between the metric value of the reference company and the mean of the respective set of benchmark companies by the standard deviation of the respective set of benchmark companies.
Forthman discloses to calculate the z-score for the reference company, the one or more processors are configured to divide a difference between the metric value of the reference company and the mean of the respective set of benchmark companies by the standard deviation of the respective set of benchmark companies.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Raikula and Hardtke to calculate the z-score for the reference company, the one or more processors are configured to divide a difference between the metric value of the reference company and the mean of the respective set of benchmark companies by the standard deviation of the respective set of benchmark companies as taught by Forthman since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Raikula, Hardtke, and Forthman teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 13, see discussion of claim 1 above, while Raikula in view of Hardtke teaches the system above, Raikula in view of Hardtke does not explicitly disclose a system having the limitations of, however,
Forthman teaches to calculate the collective index score for the reference company, the one or more processors are configured to calculate a mean of the individual index scores for the individuals associated with the reference company (see; par. [0054] of Forthman teaches a module computes an individual "leverage score" for each user. Par. [0055] further teaches The module applies the leverage score for the individual user... the module transmits the affluence and influence adjusted network value score... to the system for incorporation into the process of generating a social media health index for companies. This teaches the upward aggregation of individual scores into a company-level index the structural parallel to aggregating individual index scores into a collective index score. Par. [0027] and par. [0035] where the calculations calculate a mean and standard deviation from amongst the plurality of individuals (i.e. scores of the individual)).
The Examiner notes that Raikula teaches similar to the instant application teaches generating an index social health. Specifically, Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hardtke teaches identifying candidates for job openings using scoring function based on features in resumes and job descriptions and as it is comparable in certain respects to Raikula which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Forthman teaches composite scoring and rating methodology and as it is comparable in certain respects to Raikula and Hardtke which generating an index value related to data analysis as well as the instant application it is viewed as analogous art in that it is the analysis of data in a business environment and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Raikula and Hardtke discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data. However, Raikula and Hardtke fails to disclose to calculate the collective index score for the reference company, the one or more processors are configured to calculate a mean of the individual index scores for the individuals associated with the reference company.
Forthman discloses to calculate the collective index score for the reference company, the one or more processors are configured to calculate a mean of the individual index scores for the individuals associated with the reference company.
It would be obvious to one of ordinary skill in the art to include in the task management
(system/method/apparatus) of Raikula and Hardtke to calculate the collective index score for the reference company, the one or more processors are configured to calculate a mean of the individual index scores for the individuals associated with the reference company as taught by Forthman since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Raikula, Hardtke, and Forthman teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 17, see discussion of claim 16 above, while Raikula in view of Hardtke teaches the computer readable medium above Claim 17 recites the same or similar limitations as those addressed above in claim 8, Claim 17 is therefore rejected for the same or similar limitations as set forth above in claim 8.
Referring to Claim 20, see discussion of claim 16 above, while Raikula in view of Hardtke teaches the computer readable medium above Claim 20 recites the same or similar limitations as those addressed above in claim 13, Claim 20 is therefore rejected for the same or similar limitations as set forth above in claim 13.
Claims 10 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raikula et al. (U.S. Patent Publication 2015/0254291 A1) (hereafter Raikula) in view of Hardtke et al. (U.S. Patent Publication 2014/0122355 A1) (hereafter Hardtke) in further view of Homsi et al. (U.S. Patent Publication 2010/0129780 A1) (hereafter Homsi).
Referring to Claim 10, see discussion of claim 1 above, while Raikula in view of Hardtke teaches the system above, Raikula in view of Hardtke does not explicitly disclose a system having the limitations of, however,
Homsi teaches to assign the reference company and each of the respective set of benchmark companies to one of a plurality of buckets based on the metric value for the metric-of- interest (see; par. [0059], par. [0084] and par. [0096] of Homsi teaches sorting data to provide the capability rank of an entity against one another (i.e. benchmark)).
determine the percentile rank of the reference company based on a bucket distribution of the reference company and the respective set of benchmark companies (see; par. [0058] of Homsi teaches the normative data is sorted and transformed into percentile data, par. [0085]-[0086] of Homsi teaches a defined floor and ceiling to the value to rank the event, Abstract – preforms a percentile comparison among the different tasks, par. [0047] where comparing the results that provides the distribution of test results (i.e. benchmark)).
The Examiner notes that Raikula teaches similar to the instant application teaches generating an index social health. Specifically, Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hardtke teaches identifying candidates for job openings using scoring function based on features in resumes and job descriptions and as it is comparable in certain respects to Raikula which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Homsi teaches performance rating system and as it is comparable in certain respects to Raikula and Hardtke which generating an index value related to data analysis as well as the instant application it is viewed as analogous art in that it is the analysis of data in a business environment and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Raikula and Hardtke discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data. However, Raikula and Hardtke fails to disclose to assign the reference company and each of the respective set of benchmark companies to one of a plurality of buckets based on the metric value for the metric-of- interest, and determine the percentile rank of the reference company based on a bucket distribution of the reference company and the respective set of benchmark companies.
Homsi discloses to assign the reference company and each of the respective set of benchmark companies to one of a plurality of buckets based on the metric value for the metric-of- interest, and determine the percentile rank of the reference company based on a bucket distribution of the reference company and the respective set of benchmark companies.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Raikula and Hardtke to assign the reference company and each of the respective set of benchmark companies to one of a plurality of buckets based on the metric value for the metric-of- interest, and determine the percentile rank of the reference company based on a bucket distribution of the reference company and the respective set of benchmark companies as taught by Homsi since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Raikula, Hardtke, and Homsi teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 18, see discussion of claim 16 above, while Raikula in view of Hardtke teaches the computer readable medium above Claim 18 recites the same or similar limitations as those addressed above in claim 10, Claim 18 is therefore rejected for the same or similar limitations as set forth above in claim 10.
Claims 11, 12, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Raikula et al. (U.S. Patent Publication 2015/0254291 A1) (hereafter Raikula) in view of Hardtke et al. (U.S. Patent Publication 2014/0122355 A1) (hereafter Hardtke) in further view of Judy (U.S. Patent 8,473,320 B1).
Referring to Claim 11, see discussion of claim 1 above, while Raikula in view of Hardtke teaches the system above, Raikula in view of Hardtke does not explicitly disclose a system having the limitations of, however,
Judy teaches to calculate the individual index score for each of the individuals associated with the reference company, the one or more processors are configured to calculate a running geometric mean of the company index score of the reference company and the preceding company index scores of the preceding companies-of-employment (see; col. 22, line (58) – col. 23, line (9) of Judy teaches a Work History section where the work history comprises a listing of one or more occupations that the individual holds now or has held in the past. where col. 20, lines (28-40) provide a composite algorithm section which provides evaluating and amalgamating the sets of requirements of all the occupations in the individual's work history. Col. 5, line (62) – col. (6), line (5) teaches a grand TORQ section where a combined measure known as a Grand TORQ, which is obtained by taking a weighted average of all the TORQ values for all the individual descriptor categories measured. This teaches traversing an ordered employment history chain and aggregating scores from each position into a running composite individual score, col. 5, line (62) – col. 6, line (5) uses weighted average (i.e. where the weighted average is seen as substitution of geometric mean for weighted average in combining ratio-scale performance index scores is an obvious mathematical design choice to a person of ordinary skill).
The Examiner notes that Raikula teaches similar to the instant application teaches generating an index social health. Specifically, Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hardtke teaches identifying candidates for job openings using scoring function based on features in resumes and job descriptions and as it is comparable in certain respects to Raikula which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Judy teaches a method for statistical comparison of occupations by skill sets and other relevant attributes and as it is comparable in certain respects to Raikula and Hardtke which generating an index value related to data analysis as well as the instant application it is viewed as analogous art in that it is the analysis of data in a business environment and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Raikula and Hardtke discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data. However, Raikula and Hardtke fails to disclose to calculate the individual index score for each of the individuals associated with the reference company, the one or more processors are configured to calculate a running geometric mean of the company index score of the reference company and the preceding company index scores of the preceding companies-of-employment.
Judy discloses to calculate the individual index score for each of the individuals associated with the reference company, the one or more processors are configured to calculate a running geometric mean of the company index score of the reference company and the preceding company index scores of the preceding companies-of-employment.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Raikula and Hardtke to calculate the individual index score for each of the individuals associated with the reference company, the one or more processors are configured to calculate a running geometric mean of the company index score of the reference company and the preceding company index scores of the preceding companies-of-employment as taught by Judy since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Raikula, Hardtke, and Judy teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 12, see discussion of claim 11 above, while Raikula in view of Hardtke in further view of Judy teaches the system above, Raikula in view of Hardtke does not explicitly disclose a system having the limitations of, however,
Judy teaches the one or more processors are configured to calculate the running geometric mean with a time decay (see; col. 21, lines (40-47) of Judy teaches decay factors section where the logical purpose of developing the Decay Factors... is to reflect either the erosion of the worker's competencies... or the decline in the relevance of those skills...which may occur when that worker has been away from a particular occupation for a given time period, and a function section: the function g may be of any form including (without limiting the generality of the claim) linear, exponential, logarithmic, or the inverse of any of those. This is viewed to expressly teach applying a time-decay weighting to each occupation's score in the work history chain based on elapsed time since last employment in that occupation the exact concept of time-decaying the running aggregation of employer scores).
The Examiner notes that Raikula teaches similar to the instant application teaches generating an index social health. Specifically, Raikula discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data it is therefore viewed as analogous art in the same field of endeavor. Additionally, Hardtke teaches identifying candidates for job openings using scoring function based on features in resumes and job descriptions and as it is comparable in certain respects to Raikula which generating an index social health as well as the instant application it is viewed as analogous art and is viewed as reasonably pertinent to the problem faced by the inventor. Additionally, Judy teaches a method for statistical comparison of occupations by skill sets and other relevant attributes and as it is comparable in certain respects to Raikula and Hardtke which generating an index value related to data analysis as well as the instant application it is viewed as analogous art in that it is the analysis of data in a business environment and is viewed as reasonably pertinent to the problem faced by the inventor. This provides support that it would be obvious to combine the references to provide an obviousness rejection.
Raikula and Hardtke discloses the receiving social media data from a plurality of social media sources and determine dimensions of the social media data. However, Raikula and Hardtke fails to disclose the one or more processors are configured to calculate the running geometric mean with a time decay.
Judy discloses the one or more processors are configured to calculate the running geometric mean with a time decay.
It would be obvious to one of ordinary skill in the art to include in the task management (system/method/apparatus) of Raikula and Hardtke the one or more processors are configured to calculate the running geometric mean with a time decay as taught by Judy since the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable. Additionally, Raikula, Hardtke, and Judy teach the collecting and analysis of data in order to maximize the utilization of resource using associated tasks and they do not contradict or diminish the other alone or when combined.
Referring to Claim 19, see discussion of claim 16 above, while Raikula in view of Hardtke teaches the computer readable medium above Claim 19 recites the same or similar limitations as those addressed above in claim 11, Claim 19 is therefore rejected for the same or similar limitations as set forth above in claim 11.
Conclusion
The prior art made of record and not relied upon considered pertinent to Applicant’s disclosure.
Pande et al. (U.S. Patent 11,120,403 B2) discloses a career analytics platform.
Shacham et al. (U.S. Patent Publication 2018/0005192 A1) discloses deriving multi-level seniority of social network members.
Krishnamoorthy et al. (U.S. Patent 9,305,287 B2) discloses a score and suggestion generation for a job seeker where the score includes an estimated duration until the job seeker receives a job offer.
Gordon et al. (U.S. Patent Publication 2016/0086195 A1) discloses determine a company rank utilizing on-line social network data.
CAI (U.S. Patent Publication 2015/0161566 A1) discloses a workforce planning and analytics.
Gidwani et al. (U.S. Patent Publication 2011/0047035 A1) discloses systems, methods, and media for evaluating companies based on social performance.
Lawrence et al. (U.S. Patent Publication 2006/011193 A1) discloses a system, method for deploying computing infrastructure, and method for identifying an impact of a business action on a financial performance of a company.
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/S.S.S/Examiner, Art Unit 3625
/BETH V BOSWELL/Supervisory Patent Examiner, Art Unit 3625
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