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 .
Status of Claims
In response to communications filed on 13 March 2024, claims 1-20 are presently pending in the application, of which, claims 1, 9, and 15 are presented in independent form.
Drawings
The drawings, filed 13 March 2024, have been reviewed and accepted by the Examiner.
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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.
Regarding claims 1-20, under Step 2A claims 1-8 recite a judicial exception (abstract idea) that is not integrated into a practical application and does not provide significantly more.
Under Step 2A (prong 1), and taking claim 1 as representative, claim 1 recites:
monitoring, by a predictive action engine, digital content sources within an organization, wherein the predictive action engine is communicatively coupled to the digital content sources; and
wherein monitoring the digital content sources includes detecting generation or ingestion of digital content by computer systems of the organization;
determining, by the predictive action engine, one or more entities that are relevant to a user associated with the organization;
processing, by the predictive action engine, content items obtained from the digital content sources to detect an entity within an identified digital content item that corresponds to an entity of the one or more entities that are relevant to the user.
These limitations recite mental processes, such as concepts performed in the human mind (see: 2019 PEG, p. 52). This is because the each of the limitations above recite a series of steps that may be mentally performed by which an evaluation is made for an abstract data. For example, the limitations of ‘monitoring, by a predictive action engine, digital content sources within an organization, wherein the predictive action engine is communicatively coupled to the digital content sources; and wherein monitoring the digital content sources includes detecting generation or ingestion of digital content by computer systems of the organization; determining, by the predictive action engine, one or more entities that are relevant to a user associated with the organization; processing, by the predictive action engine, content items obtained from the digital content sources to detect an entity within an identified digital content item that corresponds to an entity of the one or more entities that are relevant to the user,’ illustrate a judgement being performed to find matching results and does not perform any technical operation. This represents a judgement or decision which are concepts performed in the human mind and falls under certain methods of mental processes. Accordingly, under step 2A (prong 1) the claim recites an abstract idea because the claim recites limitations that fall within the “Certain methods of mental processes” grouping of abstract ideas (see again: 2019 PEG, p. 52).
Under Step 2A (prong 2), the abstract idea is not integrated into a practical application. The Examiner acknowledges that representative claim 1 does recite additional elements, including hardware processing circuitry, such as edge device.
Although reciting these additional elements, taken alone or in combination these elements are not sufficient to integrate the abstract idea into a practical application. This is because the additional elements of claim 1 are recited at a high level of generality (i.e. as generic computing hardware) such that they amount to nothing more than the mere instructions to implement or apply the abstract idea on generic computing hardware (or, merely uses a computer as a tool to perform an abstract idea). Further, the additional elements do no more than generally link the use of a judicial exception to a particular technological environment or field of use (such as the Internet or computing networks).
Secondly, the additional elements are insufficient to integrate the abstract idea into a practical application because the claim fails to (i) reflect an improvement in the functioning of a computer, or an improvement to other technology or technical field, (ii) implement the judicial exception with, or use the judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, (iii) effect a transformation or reduction of a particular article to a different state or thing, or (iv) applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.
In view of the above, under Step 2A (prong 2), claim 1 does not integrate the recited exception into a practical application (see again: 2019 Revised Patent Subject Matter Eligibility Guidance).
Under Step 2B, examiners should evaluate additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself). In this case, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. That is, the limitations of ‘generating, by the predictive action engine, a custom content item for the user based on the detected entity and at least a portion of the identified digital content item,’ are additional elements that are insignificant extra solution activities that that do not amount to significantly more than the judicial exception.
Returning to representative claim 1, taken individually or as a whole the additional elements of claim 1 do not provide an inventive concept (i.e. they do not amount to “significantly more” than the exception itself). As discussed above with respect to the integration of the abstract idea into a practical application, the additional elements used to perform the claimed process amount to no more than the mere instructions to apply the exception using a generic computer and/or no more than a general link to a technological environment.
Furthermore, the additional elements fail to provide significantly more also because the claim simply appends well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception. For example, the additional elements of claim 1 utilize operations the courts have held to be well-understood, routine, and conventional (see: MPEP 2106.05(d)(lI)), including at least:
• receiving or transmitting data over a network, and/or
• storing and retrieving information in memory
• performing repetitive calculations
Even considered as an ordered combination (as a whole), the additional elements of claim 1 do not add anything further than when they are considered individually.
In view of the above, representative claim 1 does not provide an inventive concept (“significantly more”) under Step 2B, and is therefore ineligible for patenting.
Dependent claim 2 also does not integrate the abstract idea into a practical application. Notably, claim 2 recites ‘wherein determining the one or more entities that are relevant to the user comprises: detecting interactions by the user with digital content items maintained by the organization; identifying entities associated with the digital content items interacted with by the user; and selecting one or more of the identified entities as the one or more entities that are relevant to the user,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 2 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 2 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 2 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
In view of the above, claim 2 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 3 also does not integrate the abstract idea into a practical application. Notably, claim 3 recites ‘wherein determining the one or more entities that are relevant to the user comprises: detecting an interaction by the user with the custom content item; and determining the detected entity is relevant to the user based on the interaction with the custom content item,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 3 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 3 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 3 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually.
In view of the above, claim 3 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 4 also does not integrate the abstract idea into a practical application. Notably, claim 4 recites ‘wherein generating the custom content item for the user comprises: modifying one or more of the content items obtained from the digital content sources,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 4 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 4 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 4 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 4 do not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 5 also does not integrate the abstract idea into a practical application. Notably, claim 5 recites ‘wherein generating the custom content item for the user comprises: sending, to a large language model (LLM), a prompt to cause the LLM to generate the custom content item based on: one or more of the content items obtained from the digital content sources, or one or more digital content items maintained by the organization,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 5 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 5 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 5 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 5 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 6 also does not integrate the abstract idea into a practical application. Notably, claim 6 recites ‘wherein generating the custom content item for the user comprises: applying, to data indicating interactions by the user with digital content items maintained by the organization, a content type prediction model that is trained to generate a prediction for a type of content item that is relevant to the user based on the interactions with the digital content items maintained by the organization; wherein generating the custom content item comprises generating a content item of the predicted type,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 6 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 6 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 6 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 6 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 7 also does not integrate the abstract idea into a practical application. Notably, claim 7 recites ‘wherein the content type prediction model is trained based on a set of historical timelines that each include a sequence of content items associated with a workflow, and wherein the content type prediction model is configured to: receive a timeline of content items accessed by the user; and generate, as output, the prediction for the type of content item relevant to the user based on the timeline of content items accessed by the user,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 7 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 7 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 7 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 7 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Dependent claim 8 also does not integrate the abstract idea into a practical application. Notably, claim 8 recites ‘detecting a user interaction with the custom content item; and retraining the content type prediction model based on the detected user interaction,’ all which are more complexities descriptive of the abstract idea itself. Such complexities do not themselves provide further additional elements in addition to the abstract ideas themselves. Further, claim 8 relies upon at least similar additional elements that are mere instructions to implement the abstract idea or other exception on a computer. Considered both individually and as a whole, claim 8 does not integrate the recited exception into a practical application for at least similar reasons as discussed above.
Considered individually or as a whole, claim 8 also fail to result in “significantly more” than the abstract idea under step 2B. This is again because the claims merely recite additional elements that are insignificant extra-solution activity that apply the exception on generic computing hardware, generally link the exception to a technological environment, and append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception (see discussion above).
Even when viewed as an ordered combination (as a whole), the additional elements of the dependent claims do not add anything further than when they are considered individually. In view of the above, claim 8 does not provide an inventive concept (“significantly more”) under Step 2B, and are therefore ineligible for patenting.
Claims 9-14 appear to include similar subject matter as in claims 1-6 as discussed above. More specifically, independent claim 9 additionally recites ‘a non-transitory computer-readable medium having instructions stored therein…’ which is recited at a high level of generality and are recited as performing mere generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system in addition to merely indicating a field of use or technological environment in which the judicial exception do not amount to significantly more than the exception itself. All the comments made with respect to the rejection of claims 1-6 equally apply and therefore stand rejected.
Claims 15-20 appear to include similar subject matter as in claims 1-6 as discussed above. More specifically, independent claim 15 additionally recites ‘a system comprising …’ and is recited at a high level of generality and are recited as performing mere generic computer functions routinely used in computer applications. Generic computer components recited as performing generic computer functions that are well-understood, routine and conventional activities amount to no more than implementing the abstract idea with a computerized system in addition to merely indicating a field of use or technological environment in which the judicial exception do not amount to significantly more than the exception itself. All the comments made with respect to the rejection of claims 1-6 equally apply and therefore stand rejected.
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being unpatentable by Spangler, William, et al (U.S. 2019/0340294 and known hereinafter as Spangler).
As per claim 1, Spangler teaches a method comprising:
monitoring, by a predictive action engine, digital content sources within an organization (e.g. Spangler, see paragraphs [0017-0019], which discloses the system that enables users to submit queries for predictive analytics, to generate predictions based upon an analysis (e.g. ingestion) of a large corpus of data, where the digital content sources includes scientific data, scientific journals, publications, privately accessible databases, etc.), wherein the predictive action engine is communicatively coupled to the digital content sources (e.g. Spangler, see Figure 1, which illustrates a predictive system that communicates with a server system, a client system, and a plurality of databases that contain digital content resources, as further described in paragraphs [0017-0021].); and
wherein monitoring the digital content sources includes detecting generation or ingestion of digital content by computer systems of the organization (e.g. Spangler, see paragraphs [0017-0019], which discloses the system that enables users to submit queries for predictive analytics, to generate predictions based upon an analysis (e.g. ingestion) of a large corpus of data, where the digital content sources includes scientific data, scientific journals, publications, privately accessible databases, etc.);
determining, by the predictive action engine, one or more entities that are relevant to a user associated with the organization (e.g. Spangler, see paragraphs [0018-0020], which discloses the server system includes a cognitive system generate predictions based upon analysis of a large corpus of data in response to a query, where the query may be received as query inputs and a database system that stores various types of domain-relevant entities.);
processing, by the predictive action engine, content items obtained from the digital content sources to detect an entity within an identified digital content item that corresponds to an entity of the one or more entities that are relevant to the user (e.g. Spangler, see paragraphs [0021-0026], which discloses analyzing a corpus of documents to generate predictive analytics, such as predicted relationships between entities and non-entities, etc., which return results relevant to the user, see further paragraph [0031].); and
generating, by the predictive action engine, a custom content item for the user based on the detected entity and at least a portion of the identified digital content item (e.g. Spangler, see paragraphs [0045-0049], which discloses making predictions using entities and non-entities, where a collection of content is processed to extract defined entities and a semantic relationship between the objects are determined within the collection of content, which then produces a resulting data set for the user.).
As per claim 9, Spangler teaches a non-transitory, computer-readable storage medium comprising instructions recorded thereon, wherein the instructions, when executed by at least one data processor of a system, cause the system to:
monitoring, by a predictive action engine, digital content sources within an organization (e.g. Spangler, see paragraphs [0017-0019], which discloses the system that enables users to submit queries for predictive analytics, to generate predictions based upon an analysis (e.g. ingestion) of a large corpus of data, where the digital content sources includes scientific data, scientific journals, publications, privately accessible databases, etc.), wherein the predictive action engine is communicatively coupled to the digital content sources (e.g. Spangler, see Figure 1, which illustrates a predictive system that communicates with a server system, a client system, and a plurality of databases that contain digital content resources, as further described in paragraphs [0017-0021].); and
wherein monitoring the digital content sources includes detecting generation or ingestion of digital content by computer systems of the organization (e.g. Spangler, see paragraphs [0017-0019], which discloses the system that enables users to submit queries for predictive analytics, to generate predictions based upon an analysis (e.g. ingestion) of a large corpus of data, where the digital content sources includes scientific data, scientific journals, publications, privately accessible databases, etc.);
determining, by the predictive action engine, one or more entities that are relevant to a user associated with the organization (e.g. Spangler, see paragraphs [0018-0020], which discloses the server system includes a cognitive system generate predictions based upon analysis of a large corpus of data in response to a query, where the query may be received as query inputs and a database system that stores various types of domain-relevant entities.);
processing, by the predictive action engine, content items obtained from the digital content sources to detect an entity within an identified digital content item that corresponds to an entity of the one or more entities that are relevant to the user (e.g. Spangler, see paragraphs [0021-0026], which discloses analyzing a corpus of documents to generate predictive analytics, such as predicted relationships between entities and non-entities, etc., which return results relevant to the user, see further paragraph [0031].); and
generating, by the predictive action engine, a custom content item for the user based on the detected entity and at least a portion of the identified digital content item (e.g. Spangler, see paragraphs [0045-0049], which discloses making predictions using entities and non-entities, where a collection of content is processed to extract defined entities and a semantic relationship between the objects are determined within the collection of content, which then produces a resulting data set for the user.).
As per claim 15, Spangler teaches a system comprising:
at least one hardware processor (e.g. Spangler, see paragraphs [0018-0019], which discloses one or more processor coupled to memory.); and
at least one non-transitory memory storing instructions, which, when executed by the at least one hardware processor (e.g. Spangler, see paragraphs [0018-0019], which discloses one or more processor coupled to memory.), cause the system to:
monitoring, by a predictive action engine, digital content sources within an organization (e.g. Spangler, see paragraphs [0017-0019], which discloses the system that enables users to submit queries for predictive analytics, to generate predictions based upon an analysis (e.g. ingestion) of a large corpus of data, where the digital content sources includes scientific data, scientific journals, publications, privately accessible databases, etc.), wherein the predictive action engine is communicatively coupled to the digital content sources (e.g. Spangler, see Figure 1, which illustrates a predictive system that communicates with a server system, a client system, and a plurality of databases that contain digital content resources, as further described in paragraphs [0017-0021].); and
wherein monitoring the digital content sources includes detecting generation or ingestion of digital content by computer systems of the organization (e.g. Spangler, see paragraphs [0017-0019], which discloses the system that enables users to submit queries for predictive analytics, to generate predictions based upon an analysis (e.g. ingestion) of a large corpus of data, where the digital content sources includes scientific data, scientific journals, publications, privately accessible databases, etc.);
determining, by the predictive action engine, one or more entities that are relevant to a user associated with the organization (e.g. Spangler, see paragraphs [0018-0020], which discloses the server system includes a cognitive system generate predictions based upon analysis of a large corpus of data in response to a query, where the query may be received as query inputs and a database system that stores various types of domain-relevant entities.);
processing, by the predictive action engine, content items obtained from the digital content sources to detect an entity within an identified digital content item that corresponds to an entity of the one or more entities that are relevant to the user (e.g. Spangler, see paragraphs [0021-0026], which discloses analyzing a corpus of documents to generate predictive analytics, such as predicted relationships between entities and non-entities, etc., which return results relevant to the user, see further paragraph [0031].); and
generating, by the predictive action engine, a custom content item for the user based on the detected entity and at least a portion of the identified digital content item (e.g. Spangler, see paragraphs [0045-0049], which discloses making predictions using entities and non-entities, where a collection of content is processed to extract defined entities and a semantic relationship between the objects are determined within the collection of content, which then produces a resulting data set for the user.).
As per claims 2, 10, and 16, Spangler teaches the method of claim 1, the non-transitory computer-readable storage medium of claim 9, and the system of claim 15, respectively, wherein determining the one or more entities that are relevant to the user comprises:
detecting interactions by the user with digital content items maintained by the organization (e.g. Spangler, see paragraphs [0058-0059] which discloses the system may employ a monitoring system that stores and identifies information that are relevant to the user.);
identifying entities associated with the digital content items interacted with by the user (e.g. Spangler, see paragraphs [0058-0059] which discloses the system may employ a monitoring system that stores and identifies information that are relevant to the user.); and
selecting one or more of the identified entities as the one or more entities that are relevant to the user (e.g. Spangler, see paragraphs [0058-0059] which discloses the system may employ a monitoring system that stores and identifies information that are relevant to the user, where one or more relevant entities may be selected for the user.).
As per claims 3, 11, and 17, Spangler teaches the method of claim 1, the non-transitory computer-readable storage medium of claim 9, and the system of claim 15, respectively, wherein determining the one or more entities that are relevant to the user comprises:
detecting an interaction by the user with the custom content item (e.g. Spangler, see paragraphs [0058-0059] which discloses the system may employ a monitoring system that stores and identifies information that are relevant to the user, where one or more relevant entities may be selected for the user.); and
determining the detected entity is relevant to the user based on the interaction with the custom content item (e.g. Spangler, see paragraphs [0058-0059] which discloses the system may employ a monitoring system that stores and identifies information that are relevant to the user, where one or more relevant entities may be selected for the user.).
As per claims 4, 12, and 18, Spangler teaches the method of claim 1, the non-transitory computer-readable storage medium of claim 9, and the system of claim 15, respectively, wherein generating the custom content item for the user comprises:
modifying one or more of the content items obtained from the digital content sources (e.g. Spangler, see paragraphs [0058-0061], which discloses the results may be arranged and/or modified in any fashion based on rules configurable to provide the desired results to a user.).
As per claims 5, 13, and 19, Spangler teaches the method of claim 1, the non-transitory computer-readable storage medium of claim 9, and the system of claim 15, respectively, wherein generating the custom content item for the user comprises:
sending, to a large language model (LLM), a prompt to cause the LLM to generate the custom content item (e.g. Spangler, see paragraphs [0021-0026], which discloses analyzing a corpus of documents to generate predictive analytics, such as predicted relationships between entities and non-entities, etc., which return results relevant to the user, see further paragraph [0031].) based on:
one or more of the content items obtained from the digital content sources, or one or more digital content items maintained by the organization (e.g. Spangler, see paragraphs [0021-0026], which discloses analyzing a corpus of documents to generate predictive analytics, such as predicted relationships between entities and non-entities, etc., which return results relevant to the user, see further paragraph [0031].).
As per claims 6, 14, and 20, Spangler teaches the method of claim 1, the non-transitory computer-readable storage medium of claim 9, and the system of claim 15, respectively, wherein generating the custom content item for the user comprises:
applying, to data indicating interactions by the user with digital content items maintained by the organization, a content type prediction model that is trained to generate a prediction for a type of content item that is relevant to the user based on the interactions with the digital content items maintained by the organization (e.g. Spangler, see paragraphs [0018-0020], which discloses the server system includes a cognitive system generate predictions based upon analysis of a large corpus of data in response to a query, where the query may be received as query inputs and a database system that stores various types of domain-relevant entities.);
wherein generating the custom content item comprises generating a content item of the predicted type (e.g. Spangler, see paragraphs [0045-0049], which discloses making predictions using entities and non-entities, where a collection of content is processed to extract defined entities and a semantic relationship between the objects are determined within the collection of content, which then produces a resulting data set for the user.).
As per claim 7, Spangler teaches the method of claim 6, wherein the content type prediction model is trained based on a set of historical timelines that each include a sequence of content items associated with a workflow, and wherein the content type prediction model is configured to:
receive a timeline of content items accessed by the user (e.g. Spangler, see paragraphs [0058-0059] which discloses the system may employ a monitoring system that stores and identifies information that are relevant to the user, where one or more relevant entities may be selected for the user.); and
generate, as output, the prediction for the type of content item relevant to the user based on the timeline of content items accessed by the user (e.g. Spangler, see paragraphs [0045-0049], which discloses making predictions using entities and non-entities, where a collection of content is processed to extract defined entities and a semantic relationship between the objects are determined within the collection of content, which then produces a resulting data set for the user.).
As per claim 8, Spangler teaches the method of claim 6, further comprising:
detecting a user interaction with the custom content item (e.g. Spangler, see paragraphs [0058-0059] which discloses the system may employ a monitoring system that stores and identifies information that are relevant to the user, where one or more relevant entities may be selected for the user.); and
retraining the content type prediction model based on the detected user interaction (e.g. Spangler, see paragraphs [0023-0026] which discloses a machine learning system may be provided with a training data set, with which to learn and apply rules for extracting and classifying entities. A curator may review the annotations/classifications to verify that proper annotations/classifications had occurred, and additional training data may be provided.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 that includes additional prior art of record describing the general state of the art in which the invention is directed to.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAN M SYED whose telephone number is (571)272-7191. The examiner can normally be reached M-F 8:30AM-5:30PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Apu Mofiz can be reached at 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/FARHAN M SYED/Primary Examiner, Art Unit 2161 June 26, 2026
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