DETAILED ACTION
The communication is a First Action Non-Final on the merits. Claim 1, as originally filed, is currently pending and has been considered below.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
Examiner notes that no Information Disclosure Statement (IDS) was submitted. Accordingly, no Information Disclosure Statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. § 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 is rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Claim 1 is directed towards a system. Thus, the claim, on its face, is directed to one of the statutory categories of 35 U.S.C. § 101.
Step 2A - Prong One: As per MPEP 2106.04, Prong One asks does the claim recite an abstract idea, law of nature, or natural phenomenon. In Prong One examiners evaluate whether the claim(s) recites a judicial exception; that is, whether the claim(s) set forth or describe a law of nature, natural phenomenon, or abstract idea.
Claim 1 is presented here as a representative claim for specific analysis (The underlined claim terms here are interpreted as additional elements beyond the abstract idea.):
An apparatus, comprising:
a high-speed communication bus;
a memory unit communicatively coupled to the high-speed communication bus;
an integrated circuit communicatively coupled to the high-speed communication bus,
the integrated circuit to execute a machine learned model trained to receive input data and generate a set of implicit keywords from the input data to support a network service,
and an implicit facet mapper to map the input data to an implicit facet of a knowledge graph;
a database communicatively coupled to the high-speed communication bus, the database to store the set of implicit keywords and implicit facet in a data structure;
and a network interface communicatively coupled to the high-speed communication bus to access a wireless network.
The claims here are based on the recitation of an abstract idea (i.e. recitation other than the additional elements delineated here with underlining and further addressed per Step 2A - Prong Two and Step 2B). The claims recite the abstract idea of creating a knowledge graph based on implicit keywords which falls within certain methods of organizing human activity and mental processes.
The phrase "certain methods of organizing human activity" applies to 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. Refer to MPEP 2106.04(a)(2) II. A-C.
The phrase "mental processes" applies to concepts performed in the human mind (including an observation, evaluation, judgement, opinion). Refer to MPEP 2106.04(a)(2)III.A-C.
The Remaining Claims: No additional claims are currently present in this application.
Step 2A - Prong Two: As per MPEP 2106.04.II.A.2, Prong Two determines if the claim(s) recite additional elements that integrate the judicial exception into a practical application.
As for the additional elements of: a high-speed communication bus, a memory unit, an integrated circuit, a network service, a database, a network interface, and a wireless network. To be patent-eligible, the elements additional to the identified abstract idea must amount to more than "an instruction to apply the abstract idea . . . using some unspecified, generic computer" to render the claim patent-eligible. Alice Corp. v. CLS Bank Int'l, 573 U.S. 208, 226 (2014). Here, Applicant's Specification broadly describes support for well-known generic computer elements that comprise a “single machine” or “a collection of machines 900 that individually or jointly execute the instructions 1016 to perform any one or more of the methodologies discussed herein” in at least 0051. It would have been readily apparent to one having ordinary skill in the art (PHOSITA) at the time the invention was filed that the additional elements represent generic computing devices. Therefore, the claims amount to no more than a mere method, system, and/or computer program product to implement the abstract idea on a generic computer system. See MPEP § 2106.05(f).
As for the additional element(s) of: a machine learned model. The use of machine learning or artificial intelligence, without providing details of how the models themselves are improved, represents mere instructions to apply an exception. See MPEP 2106.05(f).
The ordered combination of these additional elements amounts to generally linking the use of the abstract idea to a particular technological environment or field of use (MPEP 2106.05(h)). The ordered combination offers nothing more than employing a generic configuration of computer devices and computer functions. The claims do not amount to a practical application, similar to how limiting the abstract idea in Flook to petrochemical and oil-refining industries was insufficient.
Step 2B: As per MPEP 2106.05, the additional elements are analyzed, both individually and in combination, to determine whether an "inventive concept" is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim, as a whole, amounts to significantly more than the judicial exception itself.
As for the additional element(s):
a machine learned model: Applying "machine learning" at a high level of generality represents performing repetitive calculations, Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012). Machine learning is well-understood, routine and conventional as exemplified in "Approaches to Machine Learning" by Langley et al. (Langley, P. and Carbonell, J.G. (1984), Approaches to machine learning. J. Am. Soc. Inf. Sci., 35: 306-316. https://doi.org/10.1002/asi.4630350509 (Year: 1984))
References of Record but not Applied in the Current Grounds of Rejection
The prior art listed below is made of record as considered pertinent to applicant's disclosure and is not relied upon in the grounds of rejection presented in this Office action. Those starred with '*' were added to this list in this Office action. Those without "*" were added in a previous Office action and are not repeated on a PTO-892 Notice of References Cited form, but are maintained herein for informational purposes only.
* Barbieri et al. (Pub#: WO 2014/097000 A1) discloses a system that incorporates specified keywords into user’s profile and includes “implicit keywords” in the profile based on the user’s frequently used terms.
* Kaichun et al., in “An Interactive Neural Network Approach to Keyphrase Extraction in Talent Recruitment”, discloses a system that predicts a set of key phrases, or lexical units, that conveys the core information in a document.
Examiner's Note on the Format of the Prior Art Rejections
The prior art rejections below contain underlined markings of the limitations (e.g. sample limitation). The underlined portions of a claim are addressed at the end of the grounds of rejection for that claim. Examiner notes that the underlining of the claim language is not a statement that the primary reference does not teach that language, but simply that said claim language is addressed at the end of the grounds of rejection for that claim.
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 for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 is/are rejected under 35 U.S.C. § 103 as being unpatentable over
Liang et al. (Pub. #: US 2019/0034883 A1) in view of
Mahdi et al., “Job Descriptions Keyword Extraction using Attention based Deep Learning Models with BERT”.
Claim(s) 1:
An apparatus, comprising:
a high-speed communication bus; a memory unit communicatively coupled to the high-speed communication bus; an integrated circuit communicatively coupled to the high-speed communication bus, the integrated circuit
(Liang discloses a computer that utilizes network services in coupled to a communications bus 0044, 0051, 0145 and 0146. Liang discloses a computer memory in at least 0051, 0139. Liang discloses that the computer elements may comprise "integrated circuits" in at least 0138. Also see Figures 21 and 22.)
to execute a machine learned model trained to receive input data and generate a set of implicit keywords from the input data to support a network service,
and an implicit facet mapper to map the input data to an implicit facet of a knowledge graph;
(Liang discloses, in at least 0040, that the learning method can be a machine learning method that operates by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes or mean job candidate prediction of the individual job candidate predictor decision trees in the random forest. The CP (candidate predictor) engine implements the random forest algorithm over logged interaction data from the professional social network service in order to learn a "branch label" for each brand of each job candidate predictor decision tree. The "branch label" corresponds to the "implicit facets" of the instant application. Liang discloses matching skills tree using a branch label corresponding to a skills comparison feature from the job-to-candidate feature in at least 0079. Furthermore, Liang discloses utilizing "members' behavior" on the professional social network including candidates applying to jobs and employers selecting applicants as "training data" in the machine learning models in at least 0059.)
a database communicatively coupled to the high-speed communication bus, the database to store the set of implicit keywords and implicit facet in a data structure;
(Liang discloses a database for storing information regarding members with a variety of indexes in at least 0054, 0057, and 0063. Liang discloses a communications bus within the computer in at least 0145 and 0146.)
and a network interface communicatively coupled to the high-speed communication bus to access a wireless network.
(Liang discloses a computer that utilizes network services in coupled to a communications bus 0044, 0145 and 0146 including a wireless network (i.e., "Wi-Fi" in 0145 and 0147.)
As for "to execute a machine learned model trained to receive input data and generate a set of implicit keywords from the input data to support a network service": Liang discloses utilizing "members' behavior" on the professional social network including candidates applying to jobs and employers selecting applicants as "training data" in the machine learning models in at least 0059 in support of a "search engine" 208 for matching users to job descriptions in at least 0050. Liang does not appear to specify executing a machine learning model that takes input data and generates a set of keywords in support of the network service (presumably a job postings service, but this is not claimed). However, Mahdi et al. teaches a technique of receiving job descriptions (i.e., "input data") and, using a machine learning model, generate keywords (i.e., "implicit keywords") for use in search engines (i.e., "network service") in at least Section I.
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify the job matching and search system of Liang with the technique of generating search keywords from job descriptions as taught by Mahdi. Motivation to combine Liang with Mahdi derives from both references pertaining to optimization of job matching and searching and from the desire to optimize the efficiency of a keyword from job description process (Mahdi: paragraphs 3 and 5 of Section I).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SCOTT SNIDER whose telephone number is (571)272-9604. The examiner can normally be reached M-W: 9:00-4:30 Mountain (11:00-6:30 Eastern).
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/SCOTT SNIDER/Examiner, Art Unit 3621