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 .
Specification
Applicant is reminded of the proper language and format for an abstract of the disclosure.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
The language should be clear and concise and should not repeat information given in the title. It should avoid using phrases which can be implied, such as, “The disclosure concerns,” “The disclosure defined by this invention,” “The disclosure describes,” etc. In addition, the form and legal phraseology often used in patent claims, such as “means” and “said,”, “may” should be avoided.
The abstract of the disclosure is objected to because of the phrases, as noted above. A corrected abstract of the disclosure is required and must be presented on a separate sheet, apart from any other text. See MPEP § 608.01(b).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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.
Claim(s) 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yao et al (20190130464).
As per claim 1, Yao et al (20190130464) teaches a method, comprising:
receiving first text data from a first user device associated with a service provider, wherein the first text data indicates an experience of the service provider providing a consumer service (as, identifying service providers, based on their input of skills/experience, entered as their profile – para 0024) ;
receiving second text data from a second user device associated with a consumer, wherein the second text data indicates a service requested by the consumer (as, receiving from the user, entering information via a search module – para 0018 , including type of service needed – para 0022);
determining, by an artificial intelligence (AI) engine (para 0053), a degree to which the first text data and the second text data are associated with same or similar topics; generating a match score based on the determination of the AI engine, wherein the match score indicates a degree to which the experience of the service provider matches the service requested by the consumer (as, calculating compatibility scores, based on the provider/servicer features, including skills, connections, schooling, etc. – para 0034; the compatibility scores representing a degree of match, between the provider features and the RFP (request for proposals) – see para 0033);
and outputting the match score to the first user device or the second user device (as, outputting the matching results – para 0026).
As per claim 2, Yao et al (20190130464) teaches the method of claim 1, wherein the first text data or the second text data comprises an electronic document (as, the source of provider information is reviews/endorsements, comments, etc., -- see para 0035, reflecting back on para 0034 – examiner notes that this information is stored as records – para 0030).
As per claim 3, Yao et al (20190130464) teaches the method of claim 1, wherein the first text data or the second text data comprises a natural language (NL) text – as, summaries and other commentaries, about the provider, are in natural language text format – para 0034.
As per claim 4, Yao et al (20190130464) teaches the method of claim 1, wherein the AI engine is trained to determine outcomes or intents associated with texts and whether an individual outcome matches an individual intent, the method further comprising:
determining, by the AI engine, one or more outcomes associated with the first text; determining, by the AI engine, one or more intents associated with the second text data (as, the ‘first text’ as defined in claim 1, as provider information – see para 0034, 0036,0038 – the system assigns values to these ‘provider features’ – i.e., qualifications, etc.; and using AI for such scoring – para 0053; and ‘second text’ – as analyzing not only the consumer request via RFP (para 0064), but also updating the intent of the ‘second text’ – para 0033, updating, as well as, updating the matching – para 0030);
and determining, by the AI engine (para 0053), a degree to which the one or more outcomes associated with the first text data match the one or more intents associated with the second text data, wherein the match score is further based on this determination (as, calculating compatibility scores of the provider – para 0034 matching the intended service requirement by the consumer – para 0041, 0042).
As per claim 5, Yao et al (20190130464) teaches the method of claim 1, wherein in the AI engine is trained to generate a simplified summary of a complex text, the method further comprising:
generating, by the AI engine, summary data based on the first text data, wherein the first text data is indicative of the complex text, and wherein the summary data indicates the simplified summary; and outputting the summary data to the second user device (as, generating updated information – para 0061 – the responses are used to update provider features and/or ‘other data’; the ‘other data’ includes summaries of skill sets, etc. – para 0063, reflecting back on para 0034 – see summary as part of the ‘provider features’).
As per claim 6, Yao et al (20190130464) teaches the method of claim 1, wherein the AI engine is trained to generate a technical summary of a non-technical or technical text, the method further comprising:
generating, by the AI engine, technical data based on the second text data, wherein the second text data is indicative of the non-technical or technical text, and wherein the technical data indicates the technical summary;
and outputting the technical data to the first user device (as, updating the performance metrics, provider features, and other data – para 0061, using the AI resources – para 0053).
As per claim 7, Yao et al (20190130464) teaches the method of claim 1, wherein the AI engine is further trained to replace specific entity information in the first text data or the second text data with a generic identifier in a summary, the method further comprising:
generating, by the AI engine, summary data based on the first text data or the second text data, wherein the summary data is indicative of the generic identifier in place of the specific entity information in the first text data or second text data (as, using the AI engine, to ‘generate other data used to match provider to RFP 220 – see para 0053; part of the matching process, is to analyze the response and update the provider feature or other data for the RFP, among updating other sources – para 0061).
As per claim 8, Yao et al (20190130464) teaches the method of claim 1, wherein the first text data comprises a technical document prepared for another purpose besides generating the match score (as, part of the profile features in para0034, is “business related updates” and advertising – para 0014, 0015, in addition to applying for jobs).
As per claim 9, Yao et al (20190130464) teaches the method of claim 1, wherein the second text data comprises an NL text string prepared for the purpose of generating the match score (as, part of the profile features, in para 0034, of the provider, also includes analysis of ‘summaries’ – see para 0016; which by definition, is a natural language text string).
As per claim 10, Yao et al (20190130464) teaches the method of claim 1, wherein the AI engine is further trained to identify licensing required of the service provider to provide a specific service, the method further comprising:
determining, by the AI engine, whether an individual topic associated with the second text data has a licensing requirement; and outputting the licensing requirement to the second user device (as, part of the compatibility scoring, determining topics in the category of “provider features”, which includes licensing/certifications – see para 0034; the “AI” aspect is taught in para 0053, disclosing that the scoring/ranking is performed by various types of artificial neural networks).
As per claim 11, Yao et al (20190130464) teaches the method of claim 10, wherein the AI engine is further trained to identify licensing required of the service provider to provide a specific service, the method further comprising:
receiving, from the first user device, license data associated with the service provider; determining, by the AI engine, whether an individual topic associated with the second text data has a licensing requirement (as, the compatibility scores, are based on profile features – para 0034, wherein the profile features include the topic of work experiences and skills, as well as licensing/certifications);
and generating the match score further based on whether the license date associated with the service provider indicates the service provider meets the licensing requirement (as generating compatibility scores based on a similarity between the provider – para 0041, and the requested service – further into para 0041; part of the scoring, is based on licensing/certifications – see para 0034, provider features are used to calculate compatibility scores – further into para 0034, the profile features includes certifications/licenses).
Claims 12-20 are method claims whose steps are commonly found throughout claims 1-11 and as such, these commonly found steps are similar in scope and content, to the claim features found in claims 1-11 above; therefore, these commonly found claim elements in claims 12-20 are rejected under similar rationale as presented above, against claims 1-11.
Furthermore, as a matter of completeness:
Regarding claims 12,13, and the further detailed elements towards the provider experience, licensing, and scoring, Yao et al (20190130464) teaches, along with the overcall scoring as mapped above in claims 1-11, individual compatibility scores – para 0033, for a various number of provider features, such as length of employment, certifications, and licenses – para 0034; Yao et al (20190130464) also contemplates showing the results in a graph format – para 0029.
Regarding claims 14-17 and the further detailed claim elements toward geographic region, -- see para 0034, as part of the provider features, geographic information; and matching the users/consumers geographic region – para 0036; see also para 0027 wherein location information is used from the persons device/profile – para 0055, 0036; and contacts through email/IP protocols – para 0037.
Regarding claim 18, Yao et al (20190130464) teaches individual compatibility scores, for each requirement – para 0041 [Wingdings font/0xE0] hence, if the requirement of licensing/certification is part of the RFP, then, a compatibility score is calculated for that particular licensing requirement – see para 0034, listing certification/licenses, as part of the provider feature profile.
Regarding claim 19, applying similar rationale to claim 18 above, Yao et al (20190130464) teaches, as part of the provider feature profile from which individual compatibility scores are calculated, experience and experiences summaries are evaluated – see para 0034 and 0035 “work experience”, and para 0040 showing summaries of such topics/categories.
Regarding claim 20, see rationale presented above, for similar claim scope features.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Please see related art listed on the PTO-892 form.
Furthermore, the following references were found to match certain spec/claim features in the instant invention:
Demiralp et al (20220292414) teaches matching service provider with customers, para 0014, using machine learned models – para 0016.
Rao et al (20230274126) teaching the matching of service providers with users needs, using machine learned models – para 0040.
Freese et al (20210311999) teaches service provider matching using ML models – para 0041.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael Opsasnick, telephone number (571)272-7623, who is available Monday-Friday, 9am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Mr. Richemond Dorvil, can be reached at (571)272-7602. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Michael N Opsasnick/Primary Examiner, Art Unit 2658 11/19/2025