DETAILED ACTION
Status of Claims
The present application, filed on or after 3/16/2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the Application and Claims filed 05/06/2025.
Claims 1-21 have been examined and are pending.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged. This application claims the benefit of and priority to U.S. patent application Ser. No. 18/525,245 filed on November 30, 2023, which is a continuation of US patent application Ser. No. 17/747,752 filed May 18, 2022.
(AIA ) Examiner Note
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 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were effectively filed absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned at the time a later invention was effectively filed in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention
Examiner’s Note
Adhering to the guidelines of MPEP 2173.06(II), the Examiner acknowledges that all words in a claim must be considered in judging the patentability of a claim against the prior art. In re Wilson, 424 F.2d 1382, 165 USPQ 494 (CCPA 1970). The fact that terms may be indefinite does not make the claim obvious over the prior art. However, when the terms of a claim are considered to be indefinite, at least two approaches to the examination of an indefinite claim relative to the prior art are possible.
First, where the degree of uncertainty is not great, and where the claim is subject to more than one interpretation and at least one interpretation would render the claim unpatentable over the prior art, an appropriate course of action would be for the examiner to enter two rejections: (A) a rejection based on indefiniteness under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph; and (B) a rejection over the prior art based on the interpretation of the claims which renders the prior art applicable. See, e.g., Ex parte Ionescu, 222 USPQ 537 (Bd. App. 1984). When making a rejection over prior art in these circumstances, it is important for the examiner to point out how the claim is being interpreted.
Second, where there is a great deal of confusion and uncertainty as to the proper interpretation of the limitations of a claim, it would not be proper to reject such a claim on the basis of prior art. As stated in In re Steele, 305 F.2d 859, 134 USPQ 292 (CCPA 1962), a rejection under 35 U.S.C. 103 should not be based on considerable speculation about the meaning of terms employed in a claim or assumptions that must be made as to the scope of the claims.
In the present set of claims (filed 05/06/2025), a great deal of confusion and uncertainty as to the proper interpretation of the limitations of dependent claims 4-10 and 16-20 exists. Therefore, a rejection under 35 U.S.C. 103 is not made regarding these claims because, as shown in the 35 USC § 112 rejections, speculation and conjecture must be utilized by the Office and the artisan inasmuch as the claims do not adequately reflect what the disclosed invention is under the second paragraph of 35 USC § 112. Again, see In re Steele, 305 F.2d 859, 862 (CCPA 1962) (A prior art rejection cannot be sustained if the hypothetical person of ordinary skill in the art would have to make speculative assumptions concerning the meaning of claim language.)
Claim Rejections - 35 USC § 112
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 4-10 and 16-20 are rejected under 35 U.S.C. 112(b) or (for pre-AIA ) 35 U.S.C. 112, second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor, a joint inventor, or (for pre-AIA ) the applicant regards as the invention.
Each of claims 4-10 and 16-20 have been amended in part to recite steps directed towards: “presuming”, e.g.: “…presuming that the job holder will purchase or suggest purchasing…”. However, it is not clear what physical step is being implied by this “presuming”;
Regarding this step of “presuming”, the Applicant (Remarks filed 1/6/2025, pg. 1 of parent application 18/525,245) states the following: “...the term "designating" was replaced with the term "presuming" throughout the claims. A common definition of the term "presuming" is to suppose that something is the case on the basis of probability. It is believed this term is interchangeable with the "designated as likely" language used in the specification (e.g., see numbered paragraph [006] in the specification of the subject patent application), and precisely defines the nature of the rejected elements.”
Respectfully, the Examiner notes that this the change in terminology does not ameliorate the issue. For example, the question arises: does “presuming” encompass a physical step or is this literally an intended effect for a person’s mind? e.g. does the claimed system perform this “presuming” or is applicant attempting to provide non-functional descriptive material of an intended effect on a human user – i.e. it is intended that a human user may suppose that something is the case on the basis of probability? For example, is this “presuming” supposed to be a physical step of storing data indicating probability or presumption of some fact? Or, is the system assigning an identifier to a user, e.g. within a relational storage device, which might indicate probability of job holder performing this action? Or, is the feature in question intended to mean communicating to someone this presumption albeit without a particular storage or transformation of data being implied as part of such communication? Or, is this really just non-functional descriptive material of an intended effect on a human user? Or, something else entirely?
Examiner notes that the Original disclosure (Claims, Specification, and Drawings) are completely silent in regards to this term “presumption” or “presuming” and is therefore also completely silent as to what scope of subject matter this “presumption” is intended to encompass. Therefore, there is a great deal of uncertainty when attempting to interpret these claims.
Furthermore, Examiner notes the Specification (e.g. Spec filed 11/30/2023 of parent application 18/525,245) uses the term “designate” (e.g. Spec at paragraphs [006] of 18/525,245) as follows: “…The job holder is then designated as likely to purchase or suggest purchasing the identified products and services.” The specification elsewhere (e.g. Spec at [0043] of 18/525,245) states the same idea, e.g. as follows: “…one or more products or services, or both, are identified which were used by the functional area that the job holder worked in at the last previous employing entity and not used but likely needed by the functional area that the job holder works in at current employing entity (process action 704). The job holder is then designated as likely to purchase or suggest purchasing for the current employing entity each identified product and service that was used by the functional area that the job holder worked in at the last previous employing entity (process action 706)…”
However, it is not clear whether applicant intends for the “presuming” to refer to “designating” as used in the Specification. Furthermore, even if “presuming” is intended to refer to “designating”, it is not clear whether this mean a job holder definitely “will” do something or is definitely required to do something or merely means a determination that the job holder “is likely” to do something (such as likely to make a particular type of purchase); these are completely mutually exclusive ideas. Furthermore, these passages do not clarify what “designate” encompasses as noted supra.
Finally, if the feature in question is intended to mean making a determination that the job holder “is likely” to do something, the Examiner notes that the Specification is actually completely silent as to any particular algorithm or method for making such a determination of “likelihood” or degree of “likelihood” except from perhaps the implied use of a well-known type of logical fallacy (i.e. a non-sequitur “it does not follow” fallacy) which attempts to draw the conclusion that a past action necessarily leads to some likelihood of future action; this is a common logical fallacy; i.e. there is an old and well-known adage which states the following: "the best predictor of future behavior is past behavior". Although this saying is often found to apparently be true, there is no underlying logic supporting this claim and indeed one may find many cases, or examples, where this adage/saying is not true. For example, according to “Psychology Today”: Psychological scientists who study human behavior agree that past behavior is a useful marker for future behavior - But only under certain specific conditions1. In this regard, the Examiner notes that the Specification provides no specific conditions under which such assumption as claimed is true and therefore the claim feature is either merely making a claim to an obvious and well-known logical fallacy implemented in the context of job skills or the Specification fails to provide sufficient disclosure regarding their particular algorithm / method such that a person of ordinary skill in the art would be apprised of the steps necessary to arrive at the “designation” now claimed.
Either way, the feature introduces ambiguity and confusion into the scope of what is being claimed. Therefore, for each of the aforementioned reasons, claims 4-10 and 16-20 are held to be indefinite.
Examiner notes, where there is a great deal of confusion and uncertainty as to the proper interpretation of the limitations of a claim, it would not be proper to reject such a claim on the basis of prior art. As stated in In re Steele, 305 F.2d 859, 134 USPQ 292 (CCPA 1962), a rejection under 35 U.S.C. 103 should not be based on considerable speculation about the meaning of terms employed in a claim or assumptions that must be made as to the scope of the claims. Therefore, no plausible interpretation is found for these claims.
For the aforementioned reasons, claims 4-10 and 16-20 are held to be indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor, a joint inventor, or (for pre-AIA ) the applicant regards as the invention.
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 1-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. a judicial exception) without significantly more.
Per step 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7, 2019) ("Guidance")2, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, the claims recite specific limitations which fall within at least one of the groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes) enumerated in the 2019 Revised PEG, as follows:
Per Independent claims 1, 15, and 21:
analyzing the job profile data to identify job holders that have changed jobs… [per claims 1, 21] “from one employing entity to another”, or [per claim 15] “within an employing entity”;
for each job holder found to have changed jobs […], determine if the job holder is in a job at their current employing entity that involves purchasing or recommending the purchase of products, or services, or both; and
generating a report comprising a listing for each job holder found to have changed jobs […] and determined to be in a job at their current employing entity that involves purchasing or recommending the purchase of products, or services, or both.
As noted supra, these limitations fall within at least one of the groupings of abstract ideas enumerated in the 2019 PEG. Specifically, these limitations fall within the group Certain Methods Of Organizing Human Activity (e.g. fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
That is, the steps as drafted are not technical in nature nor are they a technical solution to a technical problem. Instead, these steps are a business decision to identify a specific market segment of individuals (i.e. targeting criteria corresponding to those individuals whom are responsible for purchases on behalf of their company), for the purpose of generating generic data analytics (i.e. applicant’s step of generating report which lists each job holder found to have changed jobs and are currently in a job at their current employing entity that involves purchasing or recommending the purchase of products, or services, or both) for the purpose of targeting advertising to such individuals. Thus, the claims fall into Certain Methods of Organizing Human Activity.
Stated another way, all of the aforementioned intrinsic evidence shows that the independent claims are directed towards generic steps of matching users' profile data to desired targeting criteria (job holders that have changed jobs… [per claims 1, 21] “from one employing entity to another”, or [per claim 15] “within an employing entity”), which constitutes advertising/marketing activities and following rules or instructions which is one of certain methods of organizing human activity that are judicial exceptions. (Guidance, 84 Fed. Reg. at 52.)
Also, the claimed steps shown in italics above mimic human thought processes of planning, e.g., selecting certain information over other information, i.e., evaluation, analyzing, and creating, graphic data interpretation perceptible only in the human mind, etc... See In re TLI Commc'ns LLC Patent Litig., 823 F.3d 607,611 (Fed. Cir. 2016); FairWarning IP, LLC v. Jatric Sys., Inc., 839 F.3d 1089, 1093-94 (Fed. Cir. 2016). The Federal Circuit has held similar concepts to be abstract. For example, the Federal Circuit has held that abstract ideas include the concepts of collecting data, analyzing the data, and reporting the results of the collection and analysis, including when limited to particular content. See, e.g., Intellectual Ventures I LLC v. Capital One Fin. Corp., 850 F.3d 1332, 1340-41 (Fed. Cir. 2017) (identifying the abstract idea of organizing, displaying, and manipulating data); Elec. Power Grp., LLC v. Alstom SA., 830 F.3d 1350, 1354 (Fed. Cir. 2016) (characterizing collecting information, analyzing information by steps people go through in their minds, or by mathematical algorithms, and presenting the results of collecting and analyzing information, without more, as matters within the realm of abstract ideas). Thus, under the first prong, claims 1, 15, and 21 recites the patent-ineligible judicial exception of a mental process.
Furthermore, the mere nominal recitation of generic computer components (e.g. a job profile data analyzer comprising one or more computing devices, and a job profile data analysis computer program having a plurality of sub-programs executable by said computing device or devices, wherein the sub-programs configure said computing device or devices to perform the method as claimed), does not take the claim limitation out of the enumerated grouping. Thus, the claims recite an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Although there are additional elements, other than those noted supra, recited in the claims, none of these additional element(s) or a combination of elements as recited in the claims apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. As drafted, the claims as a whole merely describe how to generally “apply” the aforementioned concepts (using generic computer components only discussed at a very high level of generality in the Specification) and link them to a field of use (i.e. in this case targeted advertising) or, serve as insignificant extra-solution activity (e.g. data-gathering, transmittal, or reporting of data). The claimed computer components are recited at a high level of generality and are merely invoked as tools to implement the idea but are not technical in nature. Merely confining the abstract idea to a particular technological environment does not establish a practical application. (See Guidance, 84 Fed. Reg. at 54.) "A claim does not cease to be abstract for section 101 purposes simply because the claim confines the abstract idea to a particular technological environment in order to effectuate a real-world benefit." In re Mahapatra, 842 F. App'x 635,638 (Fed. Cir. 2021). Therefore, simply implementing the abstract idea on or with generic computer components, as is the case here, is not a practical application of the abstract idea.
These additional limitations are as follows: “A computer-implemented process for analyzing job profile data, the process comprising the actions of: using one or more computing devices to perform the following process actions, the computing devices being in communication with each other via a computer network whenever a plurality of computing devices is used: accessing job profile data collected over a prescribed period of time, said job profile data comprising job holder identifiers, as well as at least one of job titles, job descriptions, job locations, functional areas of an employing entity, start dates of jobs, end dates of jobs, and employing entity information including an employing entity identifier, that are associated with each job holder;
…”
However, these elements do not present a technical solution to a technical problem; i.e. Applicant’s invention is not a novel nor new technique nor technical solution for “receiving requests” nor “accessing a database” nor a new or novel method of storing information on in such a database. The additional elements do not recite a specific manner of performing any of the steps core to the already identified abstract idea. Instead, these features merely serve to generally “apply” the aforementioned concepts and link them to a field of use or are insignificant extra-solution activity to the already identified abstract idea and do not integrate the abstract idea into a practical application thereof.
Per Step 2B, the Examiner does not find that the claims provide an inventive concept, i.e., the claims do not recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception recited in the claim. As discussed with respect to Step 2A Prong Two, the additional elements in the independent claims were considered as merely serving to generally “apply” the aforementioned concepts via generically described computer components and “link” them to a field of use (i.e. targeted advertising/marketing), or as insignificant extra-solution activity. For the same reason these elements are not sufficient to provide an inventive concept; i.e. the same analysis applies here in 2B. Mere instructions to apply an exception using a generic computer component and conventional data gathering cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. "[T]he relevant question is whether the claim[] here does more than simply instruct the practitioner to implement the abstract idea ... on a generic computer." Alice, 573 U.S. at 225. It does not.
So, upon revaluating here in step 2B, these elements are determined to amount to no more than mere instructions to apply the exception using generic computer components (e.g. one or more computing devices… the computing devices being in communication with each other via a computer network whenever a plurality of computing devices is used) and/or gather and transmit data which is well-understood, routine, conventional activity in the field; i.e. note the Symantec, TLI, and OIP Techs Court decisions cited in MPEP 2106.05(d)(ll) indicate that mere receipt or transmission of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Taking the claim elements separately, the function performed by the generically recited “one or more computing devices…) at each step of the process is purely conventional. Using a computer to retrieve, select, and apply decision criteria to data and modify the data as a result amounts to electronic data query and retrieval-one of the most basic functions of a computer. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP Am. Inc. v. InvestPic, LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018) (internal citation omitted). The claims do not, for example, purport to improve the functioning of the computer itself. In addition, the claims do not effect an improvement in any other technology or technical field. Accordingly, alone and in combination, these elements do not integrate the abstract idea into a practical application, as found supra, nor provide an inventive concept, and thus the claims are not patent eligible.
As for the dependent claims, the dependent claims do recite a combination of additional elements. However, these claims as a whole, considered either independently or in combination with the parent claims, are directed towards the same abstract idea and do not serve to integrate the identified abstract idea into a practical application thereof nor do they provide an inventive concept.
For example, dependent claim 2 recites the following: “wherein the job profile data comprises start dates of jobs, end dates of jobs, and employing entity information associated with each identified job holder, and wherein the sub-program to analyze the job profile data to identify job holders that have changed jobs from one employing entity to another, comprises employing the job start dates and job end dates to identify a job holder's last previous employing entity and the job holder's current employing entity.” However, this is not technical in nature nor is it a technical solution to a technical problem. Instead, this is part of the abstract idea but not significantly more. Analyzing start and end dates already delineated within a data set regarding employers to determine start and end dates of working with employers appears to be nothing more than reading collected data. This step is not significantly more than the already identified judicial exception.
As another example, dependent claim 3 recites the following: “…wherein each listing in the report comprises the job holder identifier associated with a job holder, the identifier associated with the job holder's current employing entity, the job title associated with the job holder's current job, and the name of the functional areas of the current employing entity associated with the job holder's current job.” However, reporting a selection of already collected data is not a technical solution to a technical problem. Instead, this is insignificant extra solution activity as relates to the already identified abstract idea.
Therefore, the Examiner does not find that these additional claim limitations integrate the abstract idea into a practical application nor provide an inventive concept. Instead, these limitations, as a whole and in combination with the already recited claim elements of the parent claims, are not significantly more than the already identified abstract idea. A similar finding is found for the remaining dependent claims. Regarding the remaining dependent claims, they are, as already noted supra, directed to the same abstract idea as the independent claims. See Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat. Ass 'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014) (explaining that when all claims are directed to the same abstract idea, “addressing each claim of the asserted patents [is] unnecessary.'').
For these reasons, the claims are not found to include additional elements that are sufficient to amount to significantly more than the judicial exception and are therefore not patent ineligible.
Please see the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 (found at http://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials).
Claim Rejections - 35 USC § 103 (AIA )
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
Claims 1, 3, 15, 21 are rejected under 35 U.S.C. 103 as obvious over Tang et al. (US 2017/0024701 A1; hereinafter, "Tang") in view of Wang (US 2018/0213057 A1; hereinafter, "Wang").
Claims 1, 21:
Pertaining to claims 1 and 21 exemplified in the limitations of claim 1, as shown, Tang teaches the following
A system for analyzing job profile data, comprising:
a job profile data analyzer comprising one or more computing devices, and a job profile data analysis computer program having a plurality of sub-programs executable by said computing device or devices, wherein the sub-programs configure said computing device or devices to:
access job profile data collected over a prescribed period of time, said job profile data comprising job holder identifiers, as well as at least one of job titles, job descriptions, job locations, functional areas of an employing entity, start dates of jobs, end dates of jobs, and employing entity information including an employing entity identifier, that are associated with each job holder (Tang, see at least [0015]-[0016], teaching e.g.: “Techniques are provided for creating a mapping that maps first job-related information to second job-related information. In one technique, the job-related information includes job titles. Thus, one job title maps to one or more other job titles. The mapping may be created in one or multiple ways, including tracking changes in user profiles over time and determining when users (who hold current job positions) apply to other job positions…”; see also at least [0035] regarding other information contained within a user’s job profile.);
analyze the job profile data to identify job holders that have changed jobs from one employing entity to another (Tang, see at least [0035]-[0036], e.g.: “…A user's profile may include a first name, last name, an email address, …, one or more current and/or previous employers, one or more current and/or previous job titles, a list of skills, a list of endorsements, and/or names or identities of friends , contacts, connections of the user, and derived data… One example of how a job change indication may be created is a user updating job information in the user's profile, for example, in response to changing jobs. A user may update his/her job information immediately after changing jobs or some time much later. Job change identifier is configured to detect when an item in a user's profile changes, identify the (now previous) item, and store an association between the previous item and the current item…”);
for each job holder found to have changed jobs from one employing entity to another, determine if the job holder is in a job at their current employing entity that involves […a skill]; and generate a report comprising a listing for each job holder found to have changed jobs from one employing entity to another and determined to be in a job at their current employing entity that involves […the skill] (Tang, see at least [0026]-[0028], teaching: “…Blocks 120-150 may be performed in response to receiving (or otherwise detecting) each job change indication…” and “…At block 150, a mapping is updated based on an association between the source data item and the target data item. [For example] a single job change indication causes multiple associations to be created (e.g., (1) a job title in the user's profile and a job title in a job position and (2) a job skill in the user's profile and a job skill in the job position [determine if the job holder is in a job at their current employing entity that involves a skill]), then the same mapping may be updated based each association or each association is used to update a different mapping. In an embodiment, the mapping is a monolingual translation model... if the second user is a recruiter, then the set of recommendations [reports] may be part of a talent search that leverages the mapping to identify potential candidates for a particular job position that the recruiter is seeking to fill…”, See also Tang at [0081]-[0098] regarding “Talent Match” provided to recruiters. Examiner notes that Tang’s set of all users changing jobs, which causes a “job change indication” includes the set of all users changing jobs “from one employing entity to another”.)
Although Tang teaches the above limitations, Tang may not explicitly delve into the nuances of every possible type of data which may be stored, e.g. within his user’s job profile, as a “skill” for which his recruiter may search as the recruiter seeks to fill a particular job position, such as the recited skill of purchasing or recommending the purchase of products, or services, or both. However, regarding this specific type of data of a user profile regarding user skill, Tang in view of Wang teaches the following:
[wherein the skill is…] purchasing or recommending the purchase of products, or services, or both (Wang, see at least [0040]-[0044], teaching e.g.: “For example, consider an online social network user/member profile that includes multiple sections, each with different information about the user's professional life. The system may generate vectors t, c, k, and e based on the member's profile,… For example, consider the following summary from a member that includes five sentences and the associated profile attribute (topic) designated to each sentence by the system in parenthesis after each sentence: “15 years in Business Development, Sales and Marketing” (years of experience); “I have worked in all aspects of IT Consulting Services and Software companies, including Direct Sales, Channel Sales, Marketing, and Operations” (industry); “I consistently exceeded my quota as a sales person and in management took sales from $17.9 million in 2005 to $31 million at fiscal year-end 2007” (achievement); “I managed a national sales team of 12 sales representatives reporting to the CEO” (responsibility); “Direct Sales, Channel Sales, Marketing, and Operations” (list of skills). In this manner, the system may identify profile attributes from user profiles having summaries that can be used in generating summary templates, as well as identifying profile attributes from a user's profile to select an appropriate template from which to generate a summary for the user's profile… Profile summary templates generated by the system may be stored (220) in a database for later retrieval (230). In some embodiments, the profile summary templates are stored with categorization information to aid in matching profile summary templates to appropriate users. For example, a profile summary template may be categorized based on a profession associated with the template such that the template can be matched with profiles of users having the same profession…”; Examiner notes that those in Operations, as well as Marketing and Sales recommend the purchase of products, or services, or both to business and operations directors who are responsible for purchasing equipment for their business or company. For example, the business director of a chemical plant takes recommendations from their marketing, channel sales, and direct sales about what products (e.g. polypropylene, HDPE, polycarbonate, etc…) should be manufactured and they all work closely with Operations (e.g. plant engineers, plant operators, etc…) to recommend and ultimately specify the exact equipment to be purchased to manufacture the products which the sales and marketing personnel recommend should be produced and sold.)
Therefore, the Examiner understands that Wang’s user profile data (which is taught as including different information about the user's skills performed in their professional life, such as “…“Direct Sales, Channel Sales, Marketing, and Operations” (list of skills)…”; i.e. the user’s profile skills includes purchasing or recommending the purchase of products, or services, or both) are known elements analogous to the job profile data of Tang’s users’ (where Tang already teaches updated mappings based on associations between, e.g. “a job skill in the user's profile and a job skill in the job position [determine if the job holder is in a job at their current employing entity that involves a skill]”) and therefore such user profile data of Wang may be substituted into Tang’s system and method as element(s) of Tang’s user job profile data and therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have made such substitution to arrive at the claimed feature because per MPEP 2143(I) (B) Simple substitution of one known element for another to obtain predictable results is obvious.
Claim 15:
Pertaining to claim 15, as shown, Tang teaches the following:
A system for analyzing job profile data, comprising:
a job profile data analyzer comprising one or more computing devices, and a job profile data analysis computer program having a plurality of sub-programs executable by said computing device or devices, wherein the sub-programs configure said computing device or devices to:
access job profile data collected over a prescribed period of time, said job profile data comprising job holder identifiers, as well as at least one of job titles, job descriptions, job locations, functional areas of an employing entity, start dates of jobs, end dates of jobs, and employing entity information including an employing entity identifier, that are associated with each job holder (Tang, see at least [0015]-[0016], teaching e.g.: “Techniques are provided for creating a mapping that maps first job-related information to second job-related information. In one technique, the job-related information includes job titles. Thus, one job title maps to one or more other job titles. The mapping may be created in one or multiple ways, including tracking changes in user profiles over time and determining when users (who hold current job positions) apply to other job positions…”; see also at least [0035] regarding other information contained within a user’s job profile.) ;
analyze the job profile data to identify job holders that have changed jobs within an employing entity (Tang, see at least [0035]-[0036], e.g.: “…A user's profile may include a first name, last name, an email address, residence information, a mailing address, a phone number, one or more educational institutions attended, one or more current and/or previous employers, one or more current and/or previous job titles, a list of skills, a list of endorsements, and/or names or identities of friends , contacts, connections of the user, and derived data… One example of how a job change indication may be created is a user updating job information in the user's profile, for example, in response to changing jobs. A user may update his/her job information immediately after changing jobs or some time much later. Job change identifier is configured to detect when an item in a user's profile changes, identify the (now previous) item, and store an association between the previous item and the current item…”; Examiner notes that Tang’s set of all users changing jobs, which causes a “job change indication” includes the set of all users changing jobs “within an employing entity”);
for each job holder found to have changed jobs within an employing entity to another, determine if the job holder's current job at their employing entity involves […a skill]; and generate a report comprising a listing for each job holder found to have changed jobs within an employing entity and whose current job at their employing entity involves […the skill] (Tang, see at least [0026]-[0028], teaching: “…Blocks 120-150 may be performed in response to receiving (or otherwise detecting) each job change indication…” and “…At block 150, a mapping is updated based on an association between the source data item and the target data item. [For example] a single job change indication causes multiple associations to be created (e.g., (1) a job title in the user's profile and a job title in a job position and (2) a job skill in the user's profile and a job skill in the job position [determine if the job holder is in a job at their current employing entity that involves a skill]), then the same mapping may be updated based each association or each association is used to update a different mapping. In an embodiment, the mapping is a monolingual translation model... if the second user is a recruiter, then the set of recommendations [reports] may be part of a talent search that leverages the mapping to identify potential candidates for a particular job position that the recruiter is seeking to fill…”, See also Tang at [0081]-[0098] regarding “Talent Match” provided to recruiters. Examiner notes that Tang’s set of all users changing jobs, which causes a “job change indication” includes the set of all users changing jobs “within an employing entity”)
Although Tang teaches the above limitations, Tang may not explicitly delve into the nuances of every possible type of data which may be stored within his user’s job profile as a “skill” for which his recruiter may search as the recruiter seeks to fill a particular job position, such as the recited skill of purchasing or recommending the purchase of products, or services, or both. However, regarding this specific type of data of a user profile regarding user skill, Tang in view of Wang teaches the following:
[wherein the skill is…] purchasing or recommending the purchase of products, or services, or both (Wang, see at least [0040]-[0044], teaching e.g.: “For example, consider an online social network user/member profile that includes multiple sections, each with different information about the user's professional life. The system may generate vectors t, c, k, and e based on the member's profile,… For example, consider the following summary from a member that includes five sentences and the associated profile attribute (topic) designated to each sentence by the system in parenthesis after each sentence: “15 years in Business Development, Sales and Marketing” (years of experience); “I have worked in all aspects of IT Consulting Services and Software companies, including Direct Sales, Channel Sales, Marketing, and Operations” (industry); “I consistently exceeded my quota as a sales person and in management took sales from $17.9 million in 2005 to $31 million at fiscal year-end 2007” (achievement); “I managed a national sales team of 12 sales representatives reporting to the CEO” (responsibility); “Direct Sales, Channel Sales, Marketing, and Operations” (list of skills). In this manner, the system may identify profile attributes from user profiles having summaries that can be used in generating summary templates, as well as identifying profile attributes from a user's profile to select an appropriate template from which to generate a summary for the user's profile… Profile summary templates generated by the system may be stored (220) in a database for later retrieval (230). In some embodiments, the profile summary templates are stored with categorization information to aid in matching profile summary templates to appropriate users. For example, a profile summary template may be categorized based on a profession associated with the template such that the template can be matched with profiles of users having the same profession…” Examiner notes that those in Operations, as well as Marketing and Sales recommend the purchase of products, or services, or both to business and operations directors who are responsible for purchasing equipment for their business or company. For example, the business director of a chemical plant takes recommendations from their marketing, channel sales, and direct sales about what products (e.g. polypropylene, HDPE, polycarbonate, etc…) should be manufactured and they all work closely with Operations (e.g. plant engineers, plant operators, etc…) to recommend and ultimately specify the exact equipment to be purchased to manufacture the products which the sales and marketing personnel recommend should be produced and sold.).
Therefore, the Examiner understands that Wang’s user profile data (which is taught as including different information about the user's skills performed in their professional life, such as “…“Direct Sales, Channel Sales, Marketing, and Operations” (list of skills)…”; i.e. the user’s profile skills includes purchasing or recommending the purchase of products, or services, or both) are known elements analogous to the job profile data of Tang’s users’ (where Tang already teaches updated mappings based on associations between, e.g. “a job skill in the user's profile and a job skill in the job position [determine if the job holder is in a job at their current employing entity that involves a skill]”) and therefore such user profile data of Wang may be substituted into Tang’s system and method as element(s) of Tang’s user job profile data and therefore it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have made such substitution to arrive at the claimed feature because per MPEP 2143(I) (B) Simple substitution of one known element for another to obtain predictable results is obvious.
Claim 3:
Tang/Wang teach the above limitations upon which this claim depends. Furthermore, Tang teaches the following:
The system of Claim 1, wherein each listing in the report comprises the job holder identifier associated with a job holder, the identifier associated with the job holder's current employing entity, the job title associated with the job holder's current job, and the name of the functional areas of the current employing entity associated with the job holder's current job (Tang, see again at least [0035] in view of [0081]-[0098], e.g. regarding candidate user profile recommendation provided to a recruiter, where a user profile may include, per at least [0035] “…A user's profile may include a first name, last name, an email address, residence information, a mailing address, a phone number, one or more educational institutions
attended, one or more current and/or previous employers, one or more current and/or previous job titles, a list of skills, a list of endorsements, and/or names or identities of friends , contacts, connections of the user, and derived data that is based on actions that the candidate has taken.
Examples of such actions include jobs to which the user has applied, views of job postings, views of company pages, private messages between tile user and other users in the user's social network, and public messages that the user posted and that are visible to users outside of the user's social network…”, etc…).
Claim 2 is rejected under 35 U.S.C. 103 as obvious over Tang in view of Wang further in view of Ahn (US 2018/0039688 A1; hereinafter, "Ahn").
Claim 2:
Although Tang/Wang teach the above limitations upon which this claim depends, and Tang teaches, e.g. per at least [0097] a “candidate recommender” which “causes one or more matching candidate profiles to be presented to the recruiter… The data may initially include (1) a list of names of the "matching" candidates, (2) one or more pieces of information about each candidate, such as job title, current employer, etc., and (3) a link that, when selected, causes a profile of the corresponding candidate to be displayed…”, Tang/Wang may not teach all the nuances of the type of job profile data as recited below. However, regarding these features, Tang/Wang in view of Ahn teaches the following:
The system of Claim 1, wherein the job profile data comprises start dates of jobs, end dates of jobs, and employing entity information associated with each identified job holder, and wherein the sub-program to analyze the job profile data to identify job holders that have changed jobs from one employing entity to another, comprises employing the job start dates and job end dates to identify a job holder's last previous employing entity and the job holder's current employing entity (Ahn, see at least [0038], teaching e.g.: “…The set of user profiles may include member generated titles (e.g., job titles, honorary titles, educational titles), locations, descriptions of job history, member generated skills and corresponding descriptions, descriptions of education, and other information pertinent to the user. For example, a user may generate a user profile in the form of an expanded resume or curriculum vitae. Descriptions of job history may include current employers, previous employers, job titles, employment duties, employment duration, dates of employment, and any other suitable information relating to employment…”)
Therefore, the Examiner understands that Ahn’s user profile data includes start and end dates of jobs, and employing entity information associated with each identified profile and identifies a user’s previous and current employer, and therefore these are known data elements analogous to the job profile data of Tang’s users’ and therefore may be substituted into Tang’s system and method as element(s) of Tang’s user job profile data because per MPEP 2143(I) (B) Simple substitution of one known element for another to obtain predictable results is obvious.
Claims 11-14 are rejected under 35 U.S.C. 103 as obvious over Tang in view of Wang further in view of Beason et al. (US 2020/0134537 A1; hereinafter, "Beason").
Claim 11:
Tang/Wang teach the above limitations upon which this claim depends. Furthermore, Tang teaches the following:
The system of Claim 1, wherein the job profile data comprises job titles associated with the identified job holders (Tang, see at least [0025] and [0097], teaching, e.g.: “…a “candidate recommender” which “causes one or more matching candidate profiles to be presented to the recruiter… The data may initially include (1) a list of names of the "matching" candidates, (2) one or more pieces of information about each candidate [job holder], such as job title [job title], current employer, etc., and (3) a link that, when selected, causes a profile of the corresponding candidate to be displayed…”)
Although Tang teaches the above limitations, and as shown supra teaches a sub-program to determine if a job holder is in a job at their current employing entity that involves purchasing or recommending the purchase of products, or services, or both, comprises, and teaches identifying whether a job holder is in a job at their current employing entity that involves purchasing or recommending the purchase of products, or services, or both, Tang may not explicitly teach he uses a “job title classifier” to accomplish his identification. However, regarding use of a job title classifier as recited below, Tang/Wang in view of Beason teaches the following:
[and wherein the sub-program to determine if a job holder is in a job at their current employing entity that involves purchasing or recommending the purchase of products, or services, or both], comprises:
employing a job title classifier to identify if a job holder is in a job at their current employing entity that involves purchasing or recommending the purchase of products, or services, or both (Beason, see at least Figs. 1a, 3, 5a, and 6 and associated disclosure, e.g. [0025] and [0086] regarding classification of job titles such as at steps 613-614 as depicted in Fig. 6 classifying normalized job titles into occupational classifications)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Beason (directed towards use of a job title classifier) which is applicable to a known base device/method of Tang/Wang (already directed towards identifying whether a user [job holder] is in a job that involves purchasing, etc… including identifying a user’s “job title”) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Beason to the device/method of Tang/Wang in order to realize Tang/Wang would benefit by applying Beason’s technique to also employ a job title classifier to identify the “job title” of their user at their current employing entity and determine whether such job involves purchasing or recommending the purchase of products, or services, or both because Tang/Wang and Beason are analogous art in the same field of endeavor (at least G06Q30/02) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claim 12:
Tang/Wang/Beason teach the above limitations upon which this claim depends including, as already shown supra, a job that involves purchasing or recommending the purchase of products, or services, or both. Furthermore, Tang/Wang in view of Beason teaches the following:
The system of Claim 11, wherein employing a job title classifier, comprises: employing a supervised machine learning technique which is trained using a plurality of input-output examples, said input of each input-output example comprising a job title derived from the job profile data and said output of each input-output example comprising an indicator indicating if a job holder having the input job title of the input-output example is in a job […] wherein the job title classifier once trained comprises an inferring function that identifies if a job holder is in a job […] based on an input comprising a job title. (Beason, see at least Figs. 1a, 3, 5a, and 6 and associated disclosure, e.g. [0025] and [0086] regarding classification of job titles such as at steps 613-614 as depicted in Fig. 6 using supervised machine learning techniques to classify normalized job titles into occupational classifications; note the disclosure per at least [0037]: “…Generating different titles that at least predictably
refer to the same occupation titles can be facilitated using machine learning operations. The example environment l00A of FIG. 1a uses semi-supervised learning (e.g., supervised learning in combination with unsupervised learning). Specifically, a machine learning model trainer 102 accesses job specification seeding data 103...”)
using the job title classifier to identify whether a job holder associated with job profile data comprising a job title, which was not used to train the job title classifier, is in a job […] (Beason, see at least Figs. 4 and 5 and associated disclosures, e.g. [0065]-[0070] teaching, in part: “…shown are classifications that have descriptions that do intersect in whole or in part with one another. This is depicted by the shown "Classification1" and "Classification2". That is, the Ngram "Ngram05" is common to both "Classification1" and "Classification2". As such, it is possible that a given subject description may be classified into two or more classifications. As heretofore described, the seeded classifiers can be used as classifier/predictors [predict job using job title which was not used to train the classifier], however inasmuch as titles and descriptions change over time, the seeded learning models can be brought to currency by continuously updating the models. Several possible techniques for updating models are given in FIG. 5A and FIG. 5B…”); and
refining the job title classifier by using said supervised machine learning technique to re-train the job title classifier using input-output examples that include an input-output example comprising the job profile data comprising a job title associated with the job holder, which was not used to previously train the job title classifier, as the example input and an indicator indicating if a job holder having the input job title of the input-output example is in a job […] as the example output (Beason, see again at least [0037] as noted supra regarding “…Generating different titles that at least predictably refer to the same occupation titles can be facilitated using machine learning operations. The example environment 100A of FIG. 1A uses semi-supervised learning…” in view of [0065]-[0070], e.g.: “…however inasmuch as titles and descriptions change over time, the seeded learning models can be brought to currency by continuously updating [using supervised learning techniques as noted per para 0037] the models. Several possible techniques for updating models are given in FIG. 5A and FIG. 5B…”);
Therefore, the Examiner understands that the limitations in question are merely applying a known technique of Beason (directed towards use of a job title classifier based on supervised machine learning) which is applicable to a known base device/method of Tang/Wang (already directed towards identifying whether a user [job holder] is in a job that involves purchasing, etc… including identifying a user’s “job title”) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Beason to the device/method of Tang/Wang in order to realize Tang/Wang would benefit by applying Beason’s technique to arrive at the limitations in question because Tang/Wang and Beason are analogous art in the same field of endeavor (at least G06Q30/02) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claim 13:
Tang/Wang teach the above limitations upon which this claim depends. Furthermore, Tang teaches the following:
The system of Claim 1, wherein the job profile data comprises job titles associated with the identified job holders and functional areas of an employing entity associated with the identified job holders (Tang, see at least [0025]-[0026], teaching, e.g.: “…If a single job change indication causes multiple associations to be created (e.g., (1) a job title in the user's [job holder’s] profile and a job title in a job position and (2) a job skill [indication of functional area] in the user's profile and a job skill in the job position), then the same mapping may be updated based each association or each association is used to update a different mapping.…” and see also at least [0097] a “candidate recommender” which “causes one or more matching candidate profiles to be presented to the recruiter… The data may initially include (1) a list of names of the "matching" candidates, (2) one or more pieces of information [e.g. functional area] about each candidate [job holder], such as job title [job title], current employer [e.g. functional area], etc., and (3) a link that, when selected, causes a profile of the corresponding candidate to be displayed…”)
Although Tang teaches the above limitations, and as shown supra teaches a sub-program to determine if a job holder is in a job at their current employing entity that involves purchasing or recommending the purchase of products, or services, or both, comprises, and teaches identifying whether a job holder is in a job at their current employing entity that involves purchasing or recommending the purchase of products, or services, or both, Tang may not explicitly teach he uses a “job title classifier” to accomplish his identification. However, regarding use of a job title classifier as recited below, Tang/Wang in view of Beason teaches the following:
[and wherein the sub-program to determine if a job holder is in a job at their current employing entity that involves purchasing or recommending the purchase of products, or services, or both], comprises:
employing a job title-functional area classifier to identify if a job holder is in a job at their current employing entity that involves purchasing or recommending the purchase of products, or services, or both (Beason, see at least Figs. 1a, 3, 5a, and 6 and associated disclosure, e.g. [0025], [0037], and [0084] regarding classification of job titles in combination with using seeding data where “…seeding data includes at least a set of job titles [job titles] with corresponding one or more descriptions [functional area] pertaining to the title…”)
Therefore, the Examiner understands that the limitation in question is merely applying a known technique of Beason (directed towards use of a job title-functional area classifier) which is applicable to a known base device/method of Tang/Wang (already directed towards identifying whether a user [job holder] is in a job that involves purchasing, etc… including identifying a user’s “job title” and skill [e.g. functional area] used in that job) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Beason to the device/method of Tang/Wang in order to realize Tang/Wang would benefit by applying Beason’s technique to also employ a job title-functional area classifier to identify the “job title”, etc… of their user at their current employing entity and determine whether such job involves purchasing or recommending the purchase of products, or services, or both because Tang/Wang and Beason are analogous art in the same field of endeavor (at least G06Q30/02) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Claim 14:
Tang/Wang/Beason teach the above limitations upon which this claim depends including, as already shown supra, a job that involves purchasing or recommending the purchase of products, or services, or both. Furthermore, Tang/Wang in view of Beason teaches the following:
The system of Claim 13, wherein employing a job title-functional area classifier, comprises: employing a supervised machine learning technique which is trained using a plurality of input-output examples, said input of each input-output example comprising a
job title and functional area combination derived from the job profile data and said output of each input-output example comprising an indicator indicating if a job holder having the
input job title-functional area combination of the input-output example is in a job […] wherein the job title-functional area classifier once trained comprises an inferring function
that identifies if a job holder is in a job […] based on an input comprising a job title-functional area combination. (Beason, see at least Figs. 1a, 3, 5a and Fig. 6 depicting a machine learning model with a combination of normalized job title [job title] and “occupational classifications” [functional area], and associated disclosure, e.g. [0086]: “…another case, when the machine learning model query 607 specifies a job title (e.g., a normalized job title 613) and that the desired results are to be in the form of an occupational classification, then the shown alternate title predicate access method 608 performs database accesses to retrieve one or more occupational classifications 614 from a determined one or more of the machine learning models in the database…”; see also [0025], [0037], [0057] regarding classification of job titles using seeding data, such as depicted e.g. per steps 613-614 as depicted in Fig. 6 using supervised machine learning techniques to classify normalized job titles into occupational classifications; note the disclosure per at least [0037]: “…Generating different titles [job titles] that at least predictably refer to the same occupation titles [functional area] can be facilitated using machine learning operations. The example environment l00A of FIG. 1a uses semi-supervised learning (e.g., supervised learning in combination with unsupervised learning). Specifically, a machine learning model trainer 102 accesses job specification seeding data 103. … The seeding data includes at least a set of job titles [job title] with corresponding one or more descriptions [functional area] pertaining to the title…”)
using the job title-functional area classifier to identify whether a job holder associated with job profile data comprising a job title and functional area combination, which was not used to train the job title-functional area classifier, is in a job […] (Beason, see citations noted supra, and at least Figs. 6 and associated disclosures, e.g. [0086] in view of [0065]-[0070] the later which teaches, in part: “…As heretofore described, the seeded classifiers can be used as classifier/predictors [predict job using job title-functional classification which was not used to train the classifier], however inasmuch as titles and descriptions change over time, the seeded learning models can be brought to currency by continuously updating the models. Several possible techniques for updating models are given in FIG. 5A and FIG. 5B…”); and
refining the job title-functional area classifier by using said supervised machine learning technique to re-train the job title-functional area classifier using input output examples that include an input-output example comprising the job profile data comprising a job title and functional area combination associated with the job holder, which was not used to previously train the job title-functional area classifier, as the example input and an indicator indicating if a job holder having the input job title and functional area combination of the input-output example is in a job […] as the example output. (Beason, see again at least [0037] as noted supra regarding “…Generating different titles [job titles] that at least predictably refer to the same occupation titles [functional area] can be facilitated using machine learning operations. The example environment 100A of FIG. 1A uses semi-supervised learning…” in view of [0065]-[0070], e.g.: “…however inasmuch as titles and descriptions change over time, the seeded learning models can be brought to currency by continuously updating [using supervised learning techniques as noted per para 0037] the models. Several possible techniques for updating models are given in FIG. 5A and FIG. 5B…”);
Therefore, the Examiner understands that the limitations in question are merely applying a known technique of Beason (directed towards use of a job title-functional area classifier based on supervised machine learning) which is applicable to a known base device/method of Tang/Wang already directed towards identifying whether a user [job holder] is in a job that involves purchasing, etc… including identifying a user’s “job title” and skill [e.g. functional area] used in that job) to yield predictable results. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the techniques of Beason to the device/method of Tang/Wang in order to realize Tang/Wang would benefit by applying Beason’s technique to arrive at the limitations in question because Tang/Wang and Beason are analogous art in the same field of endeavor (at least G06Q30/02) and because according to MPEP 2143(I) (C) and/or (D), the use of known technique to improve a known device, methods, or products in the same way (or which is ready for improvement) is obvious.
Conclusion
The following prior art is made of record although not relied upon as it is considered pertinent to applicant's disclosure: US 10,515,342 B1, Haley discusses techniques for identifying referral candidates to provide referrals for applicants for employment vacancies. This disclosure is pertinent to applicant’s primary idea and may be used as an alternate reference in the rejections above.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SITTNER whose telephone number is (571)270-3984. The examiner can normally be reached M-F; ~9:30-6:30. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Waseem Ashraf can be reached on (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Michael J Sittner/
Primary Examiner, Art Unit 3621
1 https://www.psychologytoday.com/us/blog/witness/201301/the-best-predictor-future-behavior-is-past-behavior
2 In response to received public comments, the Office issued further guidance on October 17, 2019, clarifying the 2019 Revised Guidance. USPTO, October 2019 Update: Subject Matter Eligibility (the "October 2019 Update") (available at https://www.uspto.gov/sites/default/files/documents/peg_oct_2019_ update.pdf).
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