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 .
The instant application having Application No. 19/272,656 filed on 7/17/2025 is presented for examination by the Examiner. Claims 1-20 are currently pending in the present application.
Priority
As required by M.P.E.P. 201.14(c), acknowledgement is made of Applicant's claim for priority as a CON of 17/583,649 filed on 1/25/20122 now Patent 12,373,478 B2, which is a CON of 16/593,309 filed on 10/4/2019 now Patent 11,269,943 B2, which is a CIP of 16/042,902 filed on 7/26/2018 now Patent 11,113,324 B2.
Drawings
The Applicant's drawings filed on 7/17/2025 are acceptable for examination purpose.
Information Disclosure Statement
As required by M.P.E.P. 609, the Applicant's submission of the Information Disclosure Statement dated 7/17/2022 is acknowledged by the Examiner and the cited references have been considered in the examination of the claims now pending.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1 and 12 the instant application are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 and 9 of the U.S. Patent No. 11,269,943 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1 and 12 of the instant application substantially recite the limitations of claims 1 and 9 of the U.S. Patent No. 11,269,943 B2 for adding the underlined feature(s) in “performing semantic matching for each of the chunks in parallel to compare at least one occupational data record to at least one other occupational data record simultaneously and substantially in real time”.
The claims merely omit certain the underlined limitation(s) and adding the bolded limitation(s) as shown in comparison table below.
Initially, it should be noted that the instant application and the U.S. Patent number 11,269,943 B2 have the same inventive entities. The inventors and/or assignee for the US Patent and the instant application are Stefan Winzenried and Adrian Hossu as the inventors; and JANZZ Ltd as the assignee.
Instant Application
U.S. Patent 11,269,943 B2
Claim 1:
A computer-based method for determining similarity between at least two heterogenous unstructured data records and for optimizing processing performance, the method comprising:
generating, by at least one processor that is configured by executing code stored on non-transitory processor readable media, a plurality of occupational data records;
creating, by the at least one processor, for each of the occupational data records, a respective vector to represent the occupational data record;
slicing, by the at least one processor, each of the vectors into a plurality of chunks;
performing, by the at least one processor, semantic matching for each of the chunks in parallel to compare at least one occupational data record to at least one other occupational data record simultaneously and substantially in real time; and
outputting, by the at least one processor, values representing similarities between at least two of the occupational data records.
Claim 1:
A computer-based method for determining similarity between at least two heterogenous unstructured data records and for optimizing processing performance, the method comprising:
generating, by at least one processor that is configured by executing code stored on non-transitory processor readable media, a plurality of occupational data records;
creating, by the at least one processor, for each of the occupational data records, a respective vector in an n-dimensional non-orthogonal unit vector space by calculating dot products between unit vectors corresponding to concepts from an ontology to represent the occupational data record;
slicing, by the at least one processor, each of the vectors into a plurality of chunks;
performing, by the at least one processor, semantic matching for each of the chunks in parallel to compare at least one occupational data record to at least one other occupational data record; and
outputting, by the at least one processor, values representing similarities between at least two of the occupational data records.
Claim 12:
A computer-based system for determining similarity between at least two heterogenous unstructured data records and for optimizing processing performance, the system comprising:
at least one processor configured to access non-transitory processor readable media, the at least one processor further configured, when executing instructions stored on the non-transitory processor readable media, to:
generate a plurality of occupational data records;
create, for each of the occupational data records, a respective vector to represent the occupational data record;
slice each of the vectors into a plurality of chunks;
perform semantic matching for each of the chunks in parallel to compare at least one occupational data record to at least one other occupational data record simultaneously and substantially in real time; and
output values representing similarities between at least two of the occupational data records.
Claim 9:
A computer-based system for determining similarity between at least two heterogenous unstructured data records and for optimizing processing performance, the system comprising:
at least one processor configured to access non-transitory processor readable media, the at least one processor further configured, when executing instructions stored on the non-transitory processor readable media, to:
generate a plurality of occupational data records;
create, for each of the occupational data records, a respective vector in an n-dimensional non-orthogonal unit vector space by calculating dot products between unit vectors corresponding to concepts from an ontology to represent the occupational data record;
slice each of the vectors into a plurality of chunks;
perform semantic matching for each of the chunks in parallel to compare at least one occupational data record to at least one other occupational data record; and
output values representing similarities between at least two of the occupational data records.
Therefore, it would have been obvious to one of ordinary skill in the art of data processing at the time the invention was made to modify the invention as claimed in the instant application by adding the underlined feature(s) in “performing semantic matching for each of the chunks in parallel to compare at least one occupational data record to at least one other occupational data record simultaneously and substantially in real time” since the added limitation would have not changed the process according to which the method and system as claimed. Therefore, the use of having the added underlined feature(s) would be an obvious variation in the art for the purpose of achieving the same end results and would not interfere with the functionality of the steps previously claimed and would perform the same function.
The dependent claims are rejected for fully incorporating the errors of their respective base claims by dependency.
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 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claims 1 and 12, the claims recite “output values representing similarities between at least two of the occupational data records” which renders the claims indefinite. The claims provide no guidance as to how or where or when are the “values representing similarities” being generated/introduced in order to be outputted. Whether the “values representing similarities” are generated/introduced from the step(s) of “performing… to compare…”? Clarification or correction is respectfully required.
Note, the dependent claims are also rejected because they depend on and/or do not remedy the deficiencies inherited by their parent claims
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-2, 6-9, 11-13 and 17-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al. (US 2018/0150784 A1).
As per claim 1, Wang et al. discloses A computer-based method for determining similarity between at least two heterogenous unstructured data records and for optimizing processing performance, the method comprising:
generating, by at least one processor that is configured by executing code stored on non-transitory processor readable media, as (see e.g., ¶¶ 0078 and 0079; and Fig. 8) a plurality of occupational data records; as (see e.g., ¶ 0020; and Fig. 1: as “jobs 127 which may include one or more job profiles, job announcements, etc.”).
creating, by the at least one processor, for each of the occupational data records, a respective vector to represent the occupational data record; as (see e.g., ¶¶ 0018 – 0021; and Figs. 1-3: as “database storing information relating to a wide variety of entities, such as information concerning various job announcements”).
slicing, by the at least one processor, each of the vectors into a plurality of chunks; as (see e.g., ¶ 0073: as “vector component 230 parses the hidden feature vectors for each model identifying vector models having one or more hidden feature vector matching an attribute”).
performing, by the at least one processor, semantic matching for each of the chunks in parallel to compare at least one occupational data record to at least one other occupational data record simultaneously and substantially in real time; and as (see e.g., ¶ 0049: as “in some example embodiments, the similarity component 220 determines one or more first job profiles that are similar to a second job profile of the member of the social networking system 100 by comparing one or more fields of the one or more first job profiles with the second job profile and determining a score for how closely fields of the one or more fields match with fields of the second job profile”).
outputting, by the at least one processor, values representing similarities between at least two of the occupational data records, as (see e.g., ¶ 0020; and Figs. 2-4: as “similarity between a user and a job yv,m,k, an observed similarity between a user associated with a similar job and the job ya,m,k”).
As per claim 2, Wang et al. discloses The method of claim 1, wherein each of the vectors has magnitude and direction, as (see e.g., ¶ 0024; and Fig. 2: as “feature vector associated both a member profile and the job xm,k”).
As per claim 6, Wang et al. discloses The method of claim 1, further comprising applying correlation coefficients derived from information provided by an ontology, as (see e.g., ¶¶ 0020 and 0033; and Fig. 2).
As per claim 7, Wang et al. discloses The method of claim 1, further comprising weighting vectorially represented concepts, as (see e.g., ¶ 0037: as “vector model to give more weight to similar members associated with a job or members associated with a similar job”).
As per claim 8, Wang et al. discloses The method of claim 1, further comprising storing information associated with dot products that are above zero or at least equal to a predefined threshold, as (see e.g., ¶ 0054: as exceed a predetermined or dynamic threshold).
As per claim 9, Wang et al. discloses The method of claim 1, wherein the matching step includes performing asymmetric comparisons, as (see e.g., ¶ 0038: as “hidden feature vectors may be generated using the above equations and represent similarity comparisons of the job profile, one or more similar job profiles, one or more member profiles of members associated with the job profile, and one or more member profiles of members associated with the one or more similar job profiles”).
As per claim 11, Wang et al. discloses The method of claim 1, wherein the output is sorted based on degree of similarity, as (see e.g., ¶ 0049: as “similarity component 220 builds a hierarchy and ranking (inherently read on sorting) of terms to use to compare fields”).
As per claims 12 and 13, the claims are rejected under the same premises as the claims 1 and 2 respectively.
As per claims 17-20, the claims are rejected under the same premises as the claims 6-9 respectively.
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.
Claims 3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al., and further in view of Marcus et al. (US 2016/0162779 A1).
As per claim 3, Marcus et al. discloses The method of claim 1, further comprising creating an n-dimensional non-orthogonal unit vector space, which is not explicitly disclosed by Wang et al., as (Marcus et al., see e.g., ¶ 0100: as the non-orthogonal axes similar to an n-dimensional vector).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing data of the claimed invention to modify the Wang et al. invention with machine learning vector modeling, as taught by Marcus et al., to apply n-dimensional non-orthogonal unit vector as feature representing each element in each job vector (Marcus et al., ¶ 0100).
As per claim 14, the claim is rejected under the same premise as the claim 3.
Claims 5 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Wang et al., and further in view of Caid et al. (US 7,251,637 B1).
As per claim 5, Caid et al. discloses The method of claim 1, wherein each vector is in a high dimensional non-orthogonal unit vector space, which is not explicitly disclosed by Wang et al., as (Caid et al., see e.g., Col. 33 lines 46-60: as “non-orthogonal basis space”, and Col. 45 lines 49-67: as “high dimensional context vector 601”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing data of the claimed invention to modify the Wang et al. invention with context vector technique, as taught by Caid et al., to implement high dimensional non-orthogonal unit vector (Caid et al., Col. 33 lines 46-60).
As per claim 16, the claim is rejected under the same premise as the claim 5.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Wang et al., and further in view of Kothari et al. (US 2007/0143322 A1).
As per claim 105, Kothari et al. discloses The method of claim 1, wherein each vector is in a high dimensional non-orthogonal unit vector space, which is not explicitly disclosed by Wang et al., as (Kothari et al., see e.g., ¶¶ 0041 and 0066: as “this embodiment utilizes Cosine Similarity method to compare documents based on the contents of those documents”).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing data of the claimed invention to modify the Wang et al. invention with document processing technique, as taught by Kothari et al., to apply cosine similarity functionality to identify document similarity (Kothari et al., ¶ 0041).
Allowable Subject Matter
Claims 4 and 15 would be allowable if rewritten or amended to overcome the rejections, and a terminal disclaimer is filed to overcome the nonstatutory double patenting rejection as set forth in this Office action; and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2018/0150785 A1 by Wang et al. teaches generating machine learned models and recommendations identified using hidden feature vectors determined using the machine learned models. The method includes selecting a job profile associated with a first set of members of a social networking system. The method identifies a set of interactions with the job profile, where the set of actions are taken by a second set of members, and generates a vector model for the job profile. The vector model identifies a set of hidden feature vectors for the job profile. The method determines a job recommendation based on the job profile, the set of interactions, the set of second members, and the vector profile. The method then causes presentation of the job recommendation on a display device of a computing device.
US 2021/0294811 A1 by Buhrmann et al. teaches determining the semantic relatedness and meaning between at least two natural language sources is described in a prescribed context. Portions of natural languages are vectorized and mathematically processed to express relatedness as a calculated metric. The metric is associable to the natural language sources to graphically present the level of relatedness between at least two natural language sources. The metric may be re-determined with algorithms designed to compare the natural language sources with a knowledge data bank so the calculated metric can be ascertained with a higher level of certainty.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Bai D. Vu whose telephone number is (571) 270-1751. The examiner can normally be reached 9:00 - 5:30.
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/BAI D VU/Primary Examiner, Art Unit 2163 5/29/2026
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