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 AIA .
Status of Claims
This communication is a Final Office action in response to communications received on 01/09/2026. Claims 1, 2, 4-9, 12-13, 15-20 have been amended. Claim 11 has been canceled. Claim 21 has been newly added. Therefore, claims 1-10 and 12-21 are currently pending and have been addressed below.
Response to Amendment
Examiner acknowledges receipt of drawings, filed on 01/09/2026. Examiner withdraws the drawings objection for drawings 5-8F.
Claim Objections
Claims 1, 12 and 19 are objected to because of the following informality: Claims 1, 12 and 19 recite “a plurality of connection” and it should be – a plurality of connections --. Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 and 12-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Newly amended Independent claim 1 recites: “A method for enhanced digital search results for identification of entities across heterogeneous data using network graph data structure,”. Applicants’ specification does not include “heterogenous” or “search”, let alone “enhanced digital search results”, let alone “A method for enhanced digital search results for identification of entities across heterogeneous data.”
Further, newly amended independent claims 1, 12 and 19 recite: “a web-scraping engine”. While Applicant’s specification, para 0065, recites: “the data processing system may use web-scraping techniques”, this is not the same as “a web-scraping engine”. Examiner notes a web scraping engine is not the same as using web scraping techniques.
Even further, newly amended independent claims 1, 12 and 19 recite: “a search engine machine learning” model. Applicants’ specification does not include “a search engine machine learning” model. Appropriate correction is required.
Further, newly amended dependent claims 2, 6, 7, 8, 9, 13, 16, 17, 18 and 20 recite: “search engine machine learning” model. Applicants’ specification does not include “search engine machine learning” model. Appropriate correction is required.
The ‘written description’ requirement implements the principle that a patent must describe the technology that is sought to be patented; the requirement serves both to satisfy the inventor’s obligation to disclose the technologic knowledge upon which the patent is based, and to demonstrate that the patentee was in possession of the invention that is claimed." Capon v. Eshhar, 418 F.3d 1349, 1357, 76 USPQ2d 1078, 1084 (Fed. Cir. 2005). Further, the written description requirement promotes the progress of the useful arts by ensuring that patentees adequately describe their inventions in their patent specifications in exchange for the right to exclude others from practicing the invention for the duration of the patent’s term.
To satisfy the written description requirement, a patent specification must describe the claimed invention in sufficient detail that one skilled in the art can reasonably conclude that the inventor had possession of the claimed invention. See, e.g., Moba, B.V. v. Diamond Automation, Inc., 325 F.3d 1306, 1319, 66 USPQ2d 1429, 1438 (Fed. Cir. 2003); Vas-Cath, Inc. v. Mahurkar, 935 F.2d at 1563, 19 USPQ2d at 1116. However, a showing of possession alone does not cure the lack of a written description. Enzo Biochem, Inc. v. Gen-Probe, Inc., 323 F.3d 956, 969-70, 63 USPQ2d 1609, 1617 (Fed. Cir. 2002). An applicant shows possession of the claimed invention by describing the claimed invention with all of its limitations using such descriptive means as words, structures, figures, diagrams, and formulas that fully set forth the claimed invention. Lockwood v. Amer. Airlines, Inc., 107 F.3d 1565, 1572, 41 USPQ2d 1961, 1966 (Fed. Cir. 1997). The claimed invention as a whole may not be adequately described if the claims require an essential or critical feature which is not adequately described in the specification and which is not conventional in the art or known to one of ordinary skill in the art (MPEP 2163 | (A)). Dependent claims inherit the deficiencies of the parent claims and thus dependent claims are rejected on the same basis as indicated above for the respective parent claims.
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 1-10 and 12-21 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception without a practical application and significantly more.
Step 1: Identifying Statutory Categories
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (i.e., Step 1). In the instant case, claims 1-10 and 19-21 are directed to a method (i.e. a process). Claims 12-18 are directed to a system (i.e. a machine). Thus, each of these claims fall within one of the four statutory categories. Nevertheless, the claims fall within the judicial exception of an abstract idea.
Step 2A: Prong One: Abstract Ideas
Claims 1-10 and 12-21 are rejected under 35 U.S.C. 101 because the claimed invention recites an abstract idea. Independent claim 1, analogous to independent claim 12 recites: A method for enhanced search results for identification of entities across heterogeneous data comprising: establishing, a plurality of connection via one or more; receiving, over the plurality of connections from clinical data regarding a plurality of medical entities; obtain interaction data indicating interactions between the plurality of medical entities; generating a plurality of entity profiles for the plurality medical entities using the clinical data regarding the plurality of medical entities, each of the plurality of entity profiles comprising to clinical data corresponding to the respective plurality of entity profiles stored remote from the plurality of entity profiles: generating, a network graph data comprising edges between the plurality of entity profiles using the interaction data indicating interactions between the plurality of medical entities, the edges represented in the network graph data inserted into pairs of connected entity profiles within the network graph data that represent interactions between respective individuals; receiving a request comprising an identification of an event topic from a client device; retrieving the network graph data in response to the request; identifying a set of entity profiles that correspond to the event topic; generating, for each entity profile, a feature vector using an identifier of the entity profile of the identification of the event topic, the edges connected with the entity profile, and a topic affinity score of the entity profile with the topic, and using the feature vector as input into the search engine machine learning model to generate, for an event associated with the event topic, composite score for the entity profile; selecting a speaker and one or more attendees for the event based on the generated composite scores for the entity profiles; generating a record comprising associations between identifications of the speaker, the one or more attendees, and the event; and presenting the record to the client that sent the request. Independent claim 19 recites: A method comprising: establishing, a plurality of connection; receiving over the plurality of connections from the plurality, clinical data regarding a plurality of medical entities; obtain interaction data indicating interactions between the plurality of medical entities; generating, a plurality of entity profiles for the plurality medical entities using the clinical data regarding the plurality of medical entities, each of the plurality of entity profiles comprising to clinical data corresponding to the respective plurality of entity profiles stored remote from the plurality of entity profiles: generating, a network graph data comprising
The limitations as drafted, is a process that, under its broadest reasonable interpretation, falls under the abstract groupings of: Certain methods of organizing human activity (commercial or legal interactions (including 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). As the claims discuss receiving data regarding a plurality of medical entities; generating a plurality of entity profiles for the medical entities comprising relationships between the plurality of entity profiles; an event topic, and the relationships between the plurality of entity profiles to generate for an event associated with the event topic, one or more scores for the entity profiles; selecting a speaker and one or more attendees for the event based on the scores, which is a clear business relations and one of certain methods of organizing human activity. Further, dependent claims add additional limitations, for example: (claims 2 and 13) generate composite scores for entity profiles; (claims 3 and 14) wherein selecting the speaker and the one or more attendees for the event comprises: one or more composite scores; and selecting the speaker and the one or more attendees for the event; (claims 4 and 15) wherein receiving clinical data regarding the plurality of medical entity comprises retrieving a document having a plurality of authors, wherein generating the plurality of entity profiles comprises: a plurality of authors from the document; and generating relationships between the plurality of authors responsive to the plurality of authors originating from the same document; (claim 5) receiving the request comprising the identification of the event topic from a form and updating to include data of the record; (claims 6 and 16) receiving a first geographic location for the event; and identifying entity profiles that have associations with geographic locations within a distance threshold of the first geographic location (claims 7 and 17) identifying a first set of entity profiles comprising first historical attendance data indicating entities that have previously attended an event associated with the event topic and a second set of entity profiles comprising second historical attendance data indicating entities that have not previously attended any events associated with the event topic; (claims 8 and 18) generate the one or more composite scores comprises: identifying the relationships between entity profiles and the plurality of entity profiles and the relationships between the plurality of entity profiles generate one or more affinity scores for the plurality of entity profiles, calculating one or more degrees of relationship for the relationships between entity profiles based on each of the relationships; receiving historical event attendance data for the plurality of entity profiles; entity profiles and the historical event attendance data to generate one or more attendance scores; using the one or more affinity scores and the one or more attendances scores for the plurality of entity profiles as input to generate the one or more composite scores; (claim 20) generate speaker scores, attendee scores, and affinity scores for entity profiles, (claim 21) generating the topic affinity score for the entity profile by: retrieving a document containing text corresponding to a medical entity of entity using stored in the entity profile; generating a document embedding from the text of the document and a topic embedding from the identification of the topic; and executing a cosine similarity function on the document embedding and the topic embedding to generate the topic affinity score for the entity profile, but these only serve to further limit the abstract idea. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation of methods of organizing human activity, but for the recitation of generic computer components, the claims recite an abstract idea.
Step 2A: Prong Two
This judicial exception is not integrated into a practical application because the claims merely describe how to generally “apply” the abstract idea. In particular, the claims only recite the additional elements – (independent claims 1, 12 and 19) digital, processor; data sources; network graph data structure; application programming interfaces (APIs), model using identifiers; social media; web pages; web-scraping engine; data source pointers; database; search engine machine learning model; user interface; client device (claims 2, 13 and 20) trained machine learning model (claims 3 and 14) optimization model; (claims 8 and 18) affinity model; historical attendance model; a composite model. These additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Simply implementing the abstract idea on generic computer components is not a practical application of the abstract idea, as it adds the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). The limitations generally link the abstract idea to a particular technological environment or field of use (such as computing or machine learning, see MPEP 2106.05(h)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception and generally link the abstract idea to a particular technological environment or field of use. Furthermore, claims 1-10 and 12-21 have been fully analyzed to determine whether there are additional elements recited that amount to significantly more than the abstract idea. The limitations fail to include an improvement to another technology or technical field, an improvement to the functioning of the computer itself, or meaningful limitations beyond generally linking the use of the abstract idea to a particular technological environment. Thus, nothing in the claim adds significantly more to the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation. The claims are ineligible. Therefore, since there are no limitations in the claim that transform the exception into a patent eligible application such that the claim amounts to significantly more than the exception itself, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
Additional Prior Art Consulted
The prior art made of record and not relied upon which is considered pertinent to applicant’s disclosure includes the following:
Jarasius et al. US 10,592,472 B1 - Embodiments of the present disclosure relate to a database system for dynamically and automatically accessing and storing data items from multiple data sources. The system may, for example, determine data sources to access, and ways of accessing data items from those data sources, based on an indication of an analysis level and/or other analysis criteria. Further, the system may selectively and efficiently integrate data items from the multiple data sources. Selective integration of data items may be based, for example, on the indication of the analysis level and/or other analysis criteria.
Applicant is advised to review additional references supplied on the PTO-892 as to the state of the art of the invention.
Response to Arguments
Applicants arguments filed on 01/09/2026 have been fully considered but they are not persuasive. Regarding 35 U.5.C. § 101 rejections: Examiner has updated the 101 rejection in light of the most recent claim amendments and maintains the 101 rejection. Applicant’s arguments have been fully considered but are found unpersuasive.
With respect to Applicant’s remarks (page 16) “The claims, at least as amended herein, overcome the rejection under 35 U.S.C. § 101 because the claims recite an improvement in technology, and particularly an improvement to a computerized search engine.” Examiner respectfully disagrees.
Applicant’s specification does not include a “search engine”, let alone an improvement to one. See above 112 rejection. With respect to Applicants remarks on integration of the abstract idea into a practical application, the computing elements are additional elements to perform the steps and amount to no more than mere instructions to apply the exception using generic computer components. Examiner has reviewed Applicants claims and specification and has found only generic computing elements. See above 101 analysis.
With respect to Applicant’s remarks (remarks page 17), “As emphasized in the reproduction of claim 1, claim 1 recites a specific manner of generating more accurate search results of heterogeneous data extracted using APIs and a web search engine... Such improvements are analogous to the technical improvements provided by the claims in Weisner v. Google, Case No. 021-2228 (Fed. Cir. 2022). In Weisner, the Court ruled that claims directed to "improv[ing] computerized search results" are patent eligible because they solve a technical problem related to the Internet. The present claims similarly solve a computerized problem related to improving computerized search results. The claims recite the process of using APIs to retrieve data from different data sources to generate nodes of a node graph data structure and then using a web-scraping engine...”
Examiner respectfully disagrees.
As an initial matter, “web search engine” and “web-scraping engine” are not in Applicant’s specification, see above 112 rejection. Further, Applicant is merely reciting the known benefits of computing systems and known machine learning models (See Applicant’s spec, para 0080, recites a list of known machine learning models including “e.g., a support vector machine, a neural network, a random forest, etc.”). Using an already known machine learning model, is not a technical solution to a technical problem. Rather, the computing systems and machine learning models are being used for the very purpose that such computing elements are known to be used for, e.g. more efficient, faster, and etc. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
Therefore, Examiner maintains the 101 rejection with respect to these and all depending claims unless otherwise indicated.
Regarding 35 U.S.C. § 103 rejections. Applicant amended representative independent claim 1, analogous to independent claim 12, recites: “A method for enhanced digital search results for identification of entities across heterogeneous data using network graph data structure, comprising: establishing, by a processor, a plurality of connection with a plurality of data sources via one or more application programming interfaces (APIs);receiving, by processor over the plurality of connections from [[a]] the plurality of data sources, clinical data regarding a plurality of medical entities; scraping, by the processor, one or more social media web pages using a web- scraping engine to obtain interaction data indicating interactions between the plurality of medical entities; generating, by the processor, a plurality of entity profiles for the plurality medical entities using the clinical data regarding the plurality of medical entities, each of the plurality of entity profiles comprising data source pointers to clinical data corresponding to the respective plurality of entity profiles stored in a database remote from the plurality of entity profiles: generating, by the processor, a network graph data structure comprisingedges between the plurality of entity profiles using the scraped interaction data indicating interactions between the plurality of medical entities, the edges represented in the network graph data structure by pointers inserted into pairs of connected entity profiles within the network graph data structure that represent interactions between respective individuals on the scraped social media web pages; receiving, by the processor, a request comprising an identification of an event topic from a client device; retrieving, by the processor, the network graph data structure in response to the request identifying, by the processor, a set of entity profiles that correspond to the event topic; executing, by the processor, a search engine machine learning model by. generating, for each entity profile, a feature vector using an identifier of the entity profile of, the identification of the event topic, the edges connected with the entity profile, and a topic affinity score of the entity profile with the topic, and using the feature vector as input into the search engine machine learning model to generate, for an event associated with the event topic, a composite score for the entity profile; selecting, by the processor, a speaker and one or more attendees for the event based on the generated composite scores for the entity profiles; generating, by the processor, a record comprising associations between identifications of the speaker, the one or more attendees, and the event; and presenting, by the processor, the record on a user interface at the client device that sent the request”
Independent claim 19 recites: “A method comprising: establishing, by a processor, a plurality of connection with a plurality of data sources via one or more application programming interfaces (APIs);receiving, by the processor over the plurality of connections from the plurality of data sources, clinical data regarding a plurality of medical entities; scraping, by the processor, one or more social media web pages using a web- scraping engine to obtain interaction data indicating interactions between the plurality of medical entities; generating, by the processor, a plurality of entity profiles for the plurality medical entities using the clinical data regarding the plurality of medical entities, each of the plurality of entity profiles comprising data source pointers to clinical data corresponding to the respective plurality of entity profiles stored in a database remote from the plurality of entity profiles: generating, by the processor, a network graph data structure comprisingedges between the plurality of entity profiles using the scraped interaction data indicating interactions between the plurality of medical entities, the edges represented in the network graph data structure by pointers inserted into pairs of connected entity profiles within the network graph data structure that represent interactions between respective individuals on the scraped social media web pages; receiving, by the processor, a request comprising an identification of an event topic from a client device; retrieving, by the processor, the network graph data structure in response to the request identifying, by the processor, a set of entity profiles that correspond to the event topic: executing, by the processor, a search engine machine learning model by generating, for each entity profile, a feature vector using an identifier of the entity profile, the identification of the event topic, and the edges connected with the set of entity profiles, and a topic affinity score of the entity profile with the topic, and using the feature vector as input as input into the search engine machine learning model to generate, for an event associated with the event topic, a speaker score, an attendee score, and an affinity score for the entity profile; selecting, by the processor, a speaker and one or more attendees for the event based on the generated one or more speaker scores, one or more attendee scores, and one or more affinity scores; generating, by the processor, a record comprising associations between identifications of the speaker, the one or more attendees, and the event; and presenting, by the processor, the record on a user interface at the client device that sent the request. The closest prior art Pinzon (US 2021/0133898 A1), teaches a system according to one embodiment includes an enterprise system configured to provide an enterprise-wide integrated solution for pharmaceutical companies including data integration, template-driven communication, and event planning with end-to-end legal compliance and validation, wherein the enterprise-wide integrated solution includes at least a representative portal and a speaker portal, a representative device of a pharmaceutical company representative configured to communicate with the enterprise system to interact with the representative portal, and a speaker device of a key opinion leader configured to communicate with the enterprise system to interaction with the speaker portal, wherein the representative portal includes at least a programs section that allows the representative to plan a program and a speakers section that allows the representative to view profiles of a plurality of healthcare providers identified as key opinion leaders.
Closest prior art Kehal et al (US 2022/0245158 A1), teaches A system for making recommendations and predictions to an end-user of a virtual event or hybrid event is disclosed, including a virtual event platform to provide an interactive user interface for a virtual event. A recommendation and analytics server receives a plurality of interests and transmit the plurality of interests to an analytics engine and recommendation engine. The analytics engine and recommendation engine provide a response including one or more results to the user. The recommendation and prediction engine comprises a plurality of machine learning models to generate one or more recommendations, one or more predictions, and one or more matching scores to a user.
Yet, the prior art does not teach the newly amended limitations of independent claims above. After consideration of Applicants arguments, and conducting an updated prior art and non-patent literature (NPL) search, the examiner has yet to find applicable references for these limitations. Thus, prior art rejections have been withdrawn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REBECCA R NOVAK whose telephone number is (571)272-2524. The examiner can normally be reached Monday - Friday 8:30am - 5:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Lynda Jasmin can be reached on (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/R.R.N./ Examiner, Art Unit 3629
/ANDREW B WHITAKER/ Primary Examiner, Art Unit 3629
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