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 . Claims 1-11, 14-15, and 17-22 are pending.
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 1-11, 14-15, and 17-22 are rejected under 35 USC § 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is directed to a method (i.e., a process). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claim 1 is:
receiving a selection of a patent application;
aggregating a first collection of citations of the selected patent application, wherein the first collection of citations comprise backwards citations, forwards citations, or both of the selected patent application;
aggregating a second collection of citations based on the first collection of citations, wherein the second collection of citations comprise backwards citations, forwards citations, or both of the first collection of citations;
automatically extracting data from the first collection of citations, the second collection of citations, and from documents associated with the selected patent application;
generating and applying annotations to the first collection of citations, the second collection of citations, and the documents associated with the selected patent application, wherein generating and applying annotations comprises using historical patent documents and citations;
classify the extracted data to predict classifications for the extracted data; and
apply the predicted classifications as annotations to the first collection of citations, the second collection of citations, and the documents associated with the selected patent application;
reducing the second collection of citations to a subset of citations, wherein the subset is selected from the second collection of citations based on a patent relevance determination;
analyzing the annotations applied to each citation in the subset of citations to compare a set of claims from one citation in the subset of citations to a plurality of claim sets from different citations in the subset of citations to determine an amount of language overlap between the set of claims from the one citation and the plurality of claim sets from different citations in the subset;
producing a uniqueness value based on the amount of language overlap, wherein the uniqueness value decreases as the amount of overlap increases;
reiterating aggregation and reduction to produce a revised subset of citations based on one or more predetermined parameters;
producing a landscape based on the revised subset of citations; and
presenting the landscape for a user.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, gathering and refining patent citations, i.e., a judgment or evaluation, to evaluate the relevance and overlap of related documents. If a claim limitation, under its broadest reasonable interpretation (BRI), covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the claim is directed to managing and evaluating information related to intellectual property assets. This is further supported by paragraph 0014 of applicant’s specification as filed. If a claim limitation, under its BRI, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP §2106.04.
This judicial exception is not integrated into a practical application because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP §2106.05(f).
Claim 1 recites the following additional elements: using a machine learning model trained on a database; on a user interface. These elements are merely instructions to apply the abstract idea to a computer, per MPEP §2106.05(f). Applicant has only described generic computing elements in their specification, as seen in paragraphs 0504 and 0506 of applicant’s specification as filed, for example. Further, any “machine learning” features are claimed in a results-oriented manner, and the combination of these elements is nothing more than a generic computing system.
Accordingly, these additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP §2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two on the considerations discussed in MPEP §2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitates the tasks of the abstract idea, as described in MPEP §2106.05(f), and any “machine learning” features are claimed in a results-oriented manner.
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
Further, the analysis takes into consideration all dependent claims as well:
Claims 2 and 4 further narrow the abstract idea with additional steps and/or description, in addition to including additional element: database. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Claim 3 further narrows the abstract idea with additional steps and/or description, in addition to including additional elements: electronic. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Regarding claims 5-7, 9, 11, 14-15, and 17-22, applicant further narrows the abstract idea with additional step(s). There are no further additional elements to consider, beyond those highlighted above. This further narrowing of the abstract idea, similar to above, is also not patent eligible. See MPEP §2106.05(f).
Claims 8 and 10 further narrow the abstract idea with additional steps and/or description, in addition to including additional element: on the user interface. Examiner notes that this is an example of “apply it” and is simply being used to facilitate the tasks of the abstract idea. This further narrowing of the abstract idea, along with the elements alone and in combination, is not enough to demonstrate integration into practical and is not significantly more. See MPEP §2106.05(f).
Accordingly, claims 1-11, 14-15, and 17-22 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-11, 14-15, and 17-22 are rejected under 35 U.S.C. § 103 as being unpatentable over Lundberg (US 20140317000) in view of Zholudev (US 20160342591) in further view of Basak (US 20210406771).
Claim 1
Regarding claim 1, Lundberg discloses:
An automated method for determining a uniqueness value of patent citation collections, the method comprising {“FIG. 6 is a flow diagram of a method of determining a similarity score between patent documents, according to an example embodiment.” (paragraph 0010).}
receiving a selection of a patent application {“[I]nput module 214 is configured to receive input from one or more user interface elements. For example patent management system 102 may present multiple user interfaces to a user. These user interfaces may enable users to input data directly into databases 202-210, instruct the patent management system to retrieve data from patent data stores, and instruct the patent management system to perform various operations (e.g., analysis) on the data in databases 202-210.” (paragraph 0043)}
aggregating a first collection of citations of the selected patent application, wherein the first collection of citations comprise backwards citations, forwards citations, or both of the selected patent application {The system identifies a “matter” as a patent or application. (paragraph 0022) Each matter is associated with cited prior art references (backward citations) and tracks citations made to the matter (forward citations) (paragraphs 0030, 0088, 0150, and 0153) Citation data is aggregated from internal and external sources into an operations database. (paragraph 0025) This data is used for analytics, including citations timelines, rankings, and overlap analyses. (paragraphs 0036 – 0039)}
aggregating a second collection of citations based on the first collection of citations, wherein the second collection of citations comprise backwards citations, forwards citations, or both of the first collection of citations {Once a first collection of citations (e.g., the prior art cited on a selected patent or application) is identified, the system further tracks forward citations of those prior art references, i.e., a combined or aggregated second collection of citations based on the first collection of citations. (paragraph 0150) Additionally, the system supports multi-level forward/backward citation search and presentation. (paragraph 0155)}
reducing the second collection of citations to a subset of citations, wherein the subset is selected from the second collection of citations based on a patent relevance determination {The system performs patent relevance determinations using analytics based on keyword/key phrase overlap between prior art and target patents. (paragraph 0131) Also, it identifies highly cited patents, top cited inventors, and owners as indicators of significance. It analyzes citation data to identify a subset of the most relevant references, including the top ten most cited patents, inventors, and prior art owners, i.e., the subset further representing a reduced second collection of citations. (paragraph 0040)}
analyzing the annotations applied to each citation in the subset of citations to compare a set of claims from one citation in the subset of citations to a plurality of claim sets from different citations in the subset of citations to determine an amount of language overlap between the set of claims from the one citation and the plurality of claim sets from different citations in the subset {The system measures similarity among claims by applying analysis based on keywords to the annotated claim text (paragraph 0211). It further compares multiple references by calculating how much their claim keywords overlap and assigns similarity ratings across the group (paragraphs 0246-0248). The system also compares one claim set to several others within the same subset by counting how many keywords from a given clam appear in other patents (paragraph 0252). The system furthers determines the degree of overlap or uniqueness between cases by identifying shared and distinct claim elements (paragraph 0216).}
producing a uniqueness value based on the amount of language overlap, wherein the uniqueness value decreases as the amount of overlap increases {The system determines overlap or uniqueness between cases by identifying shared and distinct claim elements (paragraph 0219). The system compares similarity across references by evaluating keyword overlap, and in doing so establishes the relative degree of similarity among the items being compared (paragraphs 0246-0250) (i.e., The system derives overlap and uniqueness as opposing values based on the same keyword comparison analysis).}
reiterating aggregation and reduction to produce a revised subset of citations based on one or more predetermined parameters; {The system conducts iterative analytics operations that involve multi-level citation analysis, including aggregation of forward citations of prior art, forward citations of forward citations, and repeated citation pathway tracking. (paragraphs 0088, 0150, and 0155) The system generates a “continuing stream of watch results” (i.e., a periodic reaggregation and analysis of citation data). (paragraph 0150) Additionally, the system supports filtering citation sets based on user-specified parameters such as citation frequency. (paragraph 0040)
producing a landscape based on the revised subset of citations; and {The system supports the generation of a landscape through citation timelines and heat-mapped visualizations (paragraph 0088) It also enables graphical representations such as charts and graphs. (paragraphs 0041, 0139, 0134, 0136)}
presenting the landscape on a user interface for a user. {“In various embodiments, for the tools discussed herein a user interface may be used to determine which services a user may elect.” (paragraph 0320)}
Lundberg does not disclose:
automatically extracting data from the first collection of citations, the second collection of citations, and from documents associated with the selected patent application;
generating and applying annotations to the first collection of citations, the second collection of citations, and the documents associated with the selected patent application, wherein generating and applying annotations comprises using a machine learning model trained on a database of historical patent documents and citations, wherein the machine learning model is configured to:
classify the extracted data to predict classifications for the extracted data; and
apply the predicted classifications as annotations to the first collection of citations, the second collection of citations, and the documents associated with the selected patent application.
However, Zholudev, in a similar field of endeavor directed to linking documents using citations, teaches:
automatically extracting data from the first collection of citations, the second collection of citations, and from documents associated with the selected patent application {The system automatically parses a document’s reference section (paragraphs 0016, 0044-0046), identifies multiple citations (paragraphs 0019, 0034-0036), extracts structured citation data (paragraphs 0016, 0036, 0046), and uses that information to search and retrieve documents associated with the citations (paragraphs 0016, 0039, 0047).}
generating and applying annotations to the first collection of citations, the second collection of citations, and the documents associated with the selected patent application, wherein generating and applying annotations comprises using a machine learning model trained on a database of historical patent documents and citations {The server parses multiple publications to identify citations and stores those citations in a data repository as structured entries (paragraph 0064). It then uses ML modules to identify references sections and citations based on training sets of multiple documents with reference sections and cited references identified by humans (paragraph 0033, 0035), and perform named entity recognition over citation tokens to assign labels (e.g., title, name, etc.) (paragraphs 0036-0037). An additional ML model annotates the full text of the first document (paragraph 0051). The system further extracts text excerpts and sentiment for each citation and stores these as part of the citation entries in the database (paragraphs 0065-0066).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the patent analytics features of Lundberg to include the data processing using machine learning features of Zholudev, to improve the way in which other works cited in articles is handled. (see paragraph 0003 of Zholudev).
The combination of Lundberg and Zholudev does not teach:
wherein the machine learning model is configured to:
classify the extracted data to predict classifications for the extracted data; and
apply the predicted classifications as annotations to the first collection of citations, the second collection of citations, and the documents associated with the selected patent application.
However, Basak, in a similar field of endeavor directed to evaluation of candidate subject evaluations that is based on a categorization of information associated with the candidate subject, teaches:
wherein the machine learning model is configured to: classify the extracted data to predict classifications for the extracted data {A trained classifier is applied to extracted information from communications, where the classifier assigns tags (i.e., classifications) to the extracted information based on learned models and reference data. (paragraphs 0053, 0070).}
apply the predicted classifications as annotations to the first collection of citations, the second collection of citations, and the documents associated with the selected patent application {The system supports storing and associating the assigned tags (i.e., classifications) with communications and their extracted information within a database, such that the tags function as metadata annotations tied to the corresponding documents and datasets. (paragraphs 0053, 0100, 0125).}
Therefore, it would have been obvious to one of the ordinary skills in the art to modify the combination of Lundberg and Zholudev to include the classification and tagging of extracted data features of Basak, to improve accuracy and robustness of text analysis by applying additional evaluation techniques alongside classifier tagging. (See paragraphs 0087, 0092 of Basak).
Claim 2
Regarding claim 2, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
identifying, from a database, family members of the patent application, wherein aggregating the first collection of citations comprises aggregating citations of each of the family members, wherein the citations comprise backwards citations, forwards citations, or both of each of the family members. {Each matter (patent or application) may be associated with one or more other matters in a family, including priority documents, continuations, divisionals, and foreign counterparts. “Family members may be determined according to a legal status database such as INPADOC”. Prior art references may be stored, cross-cited, and associated with related family matters, either manually or automatically. (paragraphs 0023 and 0031) Citation data, both backward and forward, is associated with each family member, and the system supports analytics across a family. (paragraph 0036)}
Claim 3
Regarding claim 3, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
wherein receiving a selection of a patent application comprises receiving an electronic communication discussing the patent application. {The system includes an input module configured to receive data from multiple sources, including user input and electronic communications such as emails and uploaded documents. Email messages with embedded identifiers (e.g., replyID) are automatically linked to specific patent matters in the docketing system. (paragraphs 0043 and 0174)}
Claim 4
Regarding claim 4, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
wherein receiving a selection of a patent application comprises scraping a new event on a database related to the patent application. {The system imports data from external sources. If data is unavailable through databases (e.g., databases associated with foreign and domestic patent offices) , it may be scraped and parsed from websites (e.g., PAIR, INPADOC). (paragraphs 0025 and 0043) These scraping operations retrieve new events or updates related to patent applications such as published patent documents, patent applications, office actions or other patent office correspondence, prior art references, etc. (paragraph 0042) The scraped data is then processed and entered into the system’s operation database. (paragraphs 0045 – 0046)}
Claim 5
Regarding claim 5, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
wherein receiving a selection of a patent application comprises identifying a patent application of interest in a received document. {The system receives documents (e.g., office actions, published applications) and uses a parsing module to extract data such as filing date, title, and claims to identify the associated patent application (i.e., identifying a patent application of interest in a received document). (paragraphs 0045 – 0046)}
Claim 6
Regarding claim 6, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
wherein the citations comprise at least one patent or patent publication, each of the citations comprising at least one claim. {Citations include patents and published applications and that claim sets are extracted and stored for these documents. (paragraph 0030)}
Claim 7
Regarding claim 7, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
further comprising analyzing the landscape. {A landscape is disclosed including citation timelines, heat maps, and keyword overlap charts based on aggregation citation data. (paragraphs 0088 and 0127 - 0140) Additionally, analytics are applied to these visualizations, including ranking, scoring, and identifying top cited patents and inventors. (paragraphs 0040 and 0049)}
Claim 8
Regarding claim 8, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
reviewing at least one claim from the set of claims from the one citation in the subset of citations and at least one claim from each of the plurality of claim sets from different citations in the subset of citations to determine claim language {The system analyzes claim text by using keyword analysis which requires reviewing the language of the claim and the claims of other references (paragraph 0211). The system further compares claims across multiple patents by examining the claim keywords present in each reference and also reviews the keywords associated with each claim and checks for their appearance in other patents (paragraphs 0246-0250).}
breaking the claim language into one or more concepts {The system can map claim language to scope concepts, technology categories, and keywords. (paragraph 0033)}
determining how often each of the one or more concepts appears in the claim language {The system performs keyword analysis to determine frequency and overlap of concepts. (paragraphs 0039 and 0127 – 0131)}
presenting, on the user interface, a chart depicting an analysis of claim language for review by a user, wherein the chart shows the one or more concepts, the claim language, and instances of use of each of the one or more concepts in the claim language. {A claim chart in the user interface is presented. It displays claims alongside scope concepts and keyword mappings, with indicators of where concepts appear. (paragraphs 0047 – 0048 and 0212)}
Claim 9
Regarding claim 9, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
further comprising producing a graphical representation of the landscape. {A landscape with visualization figures is disclosed including citation timelines, heat maps, and keyword overlap charts based on aggregation citation data. (paragraphs 0088 and 0127 - 0140)}
Claim 10
Regarding claim 10, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
reviewing the at least one claim from the set of claims from the one citation in the subset of citations and at least one claim from each of the plurality of claim sets from different citations in the subset of citations to determine claim language further comprises {The system analyzes claim text by using keyword analysis which requires reviewing the language of the claim and the claims of other references (paragraph 0211). The system further compares claims across multiple patents by examining the claim keywords present in each reference and also reviews the keywords associated with each claim and checks for their appearance in other patents (paragraphs 0246-0250).}
breaking the claim language into one or more phrases {The system can map claim language to scope concepts, technology categories, and keywords. (paragraph 0033) “The term keyword is intended to include individual keywords as well as a number of keywords grouped together making up a key phrase, for example.” (paragraph 0050)}
determining how often each of the one or more phrases appears in the claim language {The system performs keyword analysis to determine frequency and overlap of concepts. (paragraphs 0039 and 0127 – 0131) “The term keyword is intended to include individual keywords as well as a number of keywords grouped together making up a key phrase, for example.” (paragraph 0050)}
presenting, on the user interface, a chart depicting the analysis of claim language for review by a user, wherein the chart shows the one or more phrases, the claim language, and instances of use of each of the one or more phrases in the claim language {A claim chart in the user interface is presented. It displays claims alongside scope concepts and keyword mappings, with indicators of where concepts appear. (paragraphs 0047 – 0048 and 0212) “The term keyword is intended to include individual keywords as well as a number of keywords grouped together making up a key phrase, for example.” (paragraph 0050)}
Claim 11
Regarding claim 11, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
producing a graphical representation of the chart {The system allows for the generation and display of claim charts that visually associate claims with scope concepts, keywords, and technology categories, using formatted cells to indicate mappings. (paragraph 0048) These charts are presented via a graphical user interface. (paragraph 0041)}
Claim 14
Regarding claim 14, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
wherein analyzing the landscape further comprises producing a risk of infringement analysis value {A tool performs infringement risk analysis by comparing claim text of a subject patent to claims or specifications of forward citations of patents, determining whether the claim language of the subject patent is fully or mostly found in those citing documents. (paragraphs 0360 – 0363)}
Claim 15
Regarding claim 15, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
analyzing claim language from the selected patent for one or more concepts; {A tool analyzes claim text of a patent and identifies associated keywords or concepts (e.g., “keywords associated with the claim”) used for comparison purposes. (paragraphs 0252 and 0360 – 0363)}
selecting one of the citations for comparison with the selected patent application; {A forward citation of the selected patent is retrieved and chosen for comparison in infringement assessment. (paragraphs 0360 – 0362)}
analyzing claim language from the selection citation for the one or more concepts; {A comparison is made between the text of the claims from the selected citation and the selected patent’s claims, analyzing whether the same textual elements (i.e., concepts) are present. (paragraphs 0360 – 0363)}
determining each of the one or more concepts that appear in both the selected patent and the selected citation; and {The comparison includes checking for overlap in key phrases or keywords between the selected patent and citations. (paragraphs 0360 – 0363) Additionally, a tool may calculate similarity scores based on shared keywords. (paragraph 0463)}
recommending review of any of the one or more concepts that appear in both the selected patent and the selected citation, if the one or more concepts appears above a threshold level. {The likelihood of infringement is determined when there is strong keyword or claim overlap. The system recommends review (i.e., a conclusion may be drawn that the overlap is core to the described technology) when overlap is sufficient to indicate risk. (paragraphs 0360 – 0363 and 0463)}
Claim 17
Regarding claim 17, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
wherein reducing the second collection of citations to a subset of citations comprises filtering the citations by class. {“Generate comparisons between patents owned by competitors and Matters in portfolio; 1. By technology--class/subclass/other”. This represents filtering as a tool function to reduce a citation pool for comparative analysis. (paragraph 0218)}
Claim 18
Regarding claim 18, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
wherein reducing the second collection of citations to a subset of citations comprises filtering the citations by keyword. {A list of keywords is run against cited prior art. The system is also designed to identify and isolate documents (i.e., citations) that match specific keyword combinations, effectively reducing the broader collection to a subset. (paragraph 0152)}
Claim 19
Regarding claim 19, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
wherein reducing the second collection of citations to a subset of citations comprises filtering the citations by concept {A list of keywords is run against cited prior art. The system is also designed to identify and isolate documents (i.e., citations) that match specific keyword combinations, effectively reducing the broader collection to a subset. “Also, a user may search for and map concepts to cited art that are not shown.” (paragraph 0152)}
Claim 20
Regarding claim 20, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
wherein reducing the second collection of citations to a subset of citations comprises filtering the citations by assignee {Citations may be filtered by assignee (i.e., entity identified as the owner of a cited prior art reference) through analytic identifying prior art owners cited against target and competitor portfolios. (paragraphs 0141 – 0145)}
Claim 21
Regarding claim 21, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
wherein reducing the second collection of citations to a subset of citations comprises filtering the citations by date. {“[A] tool for analytics of prior Art includes: […] Show timeline with dates of prior art vs. application.” (paragraph 0127 and 0135; see also paragraph 0139)}
Claim 22
Regarding claim 22, the combination of Lundberg, Zholudev, and Basak teaches the limitations set forth above. Lundberg further discloses:
wherein reducing the second collection of citations to a subset of citations comprises filtering the citations by frequency. {“[A]n analytics tool may be used to determine prior art overlap.” (paragraph 0141)}
Response to Arguments
Applicant’s arguments filed on 3/18/2026 have been carefully considered.
Rejection under 35 U.S.C. §101
The claims, as amended, remain directed to analyzing, classifying, and annotating citation data, which constitute mental processes and methods of organizing human activity. The limitation reciting that the ML model “classifies the extracted data to predict classifications… and applies the predicted classifications as annotations” does not change the character of the claims. Under the BRI, this limitation recited generic data classification and labeling using a ML model, which is an abstract data analysis function.
Applicant’s reliance on Ex parte Desjardins is not persuasive. In Desjardins, the claims recited a specific improvement to the operation of the ML model itself. Here, the claims do not recite any specific improvement to the model (e.g., architecture, training technique, or parameter adjustment). Instead, the model is used as a tool to perform classification.
The cited specification paragraphs describing training and prediction do not overcome this deficiency, as the claims do not include corresponding limitations reflecting a technical improvement.
Further, the alleged improvement relates to analyzing patent citation data, which is an abstract idea, rather than an improvement to computer functionality. The additional elements (e.g., ML model, database interface) are generic components performing this ordinary functions.
Accordingly, the rejection under 35 U.S.C. §101 is maintained.
Rejection under 35 U.S.C. §103
Lundberg discloses determining similarity between documents based on keyword overlap across claims, abstracts, and other sections as detailed in the analysis above. Lundberg further discloses generating a “novelty rating” based on such keyword metrics (see paragraph 0254). Under the BRI, a metric based on overlap that evaluates novelty reasonably corresponds to a “uniqueness value” that decreases as overlap increases. Higher overlap indicates less distinct subject matter.
The argument regarding the “uniqueness value” is not persuasive. Lundberg already determines similarity and novelty based on keyword overlap. There is no combination or need to combine, as Lundberg is the primary reference.
In any case, the combination relies on the express teachings of the references and does not require impermissible hindsight.
The rest of Applicant’s arguments directed to the prior 35 U.S.C. §103 are moot, given that they are predicated on previously unclaimed features incorporated via amendment, which necessitated an updated search and application of new art, as seen above. Instead of restating here, examiner directs applicant’s attention to the claim analysis above.
Accordingly, the rejection under 35 U.S.C. §103 is maintained.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARLOS F MONTALVO whose telephone number is (703)756-5863. The examiner can normally be reached Monday - Friday 8:00AM - 5:30PM; First Fridays OOO.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at 571-270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/C.F.M./Examiner, Art Unit 3629 /SARAH M MONFELDT/Supervisory Patent Examiner, Art Unit 3629
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