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 . 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.
DETAILED ACTION
This Final Office Action is in response Applicant’s communication filled on 02/13/2026.
Status of Claims
Claims 1 is independent has been amended. Claims 4,10-16,19,21-23 were canceled.
Claims 1-3,5-9,17,18, and 20 are currently pending of which:
Claims 17,18,20 are withdrawn from consideration as directed to non-elected invention.
Claims 1-3, 5-9 are currently under examination and have been rejected as follows.
IDS
The information disclosure statement filed on 02/13/2026 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
Response to Arguments / Amendments
Applicant’s 02/13/2026 amendment necessitated new grounds of rejection in this action.
Response to 112(a) Arguments / Amendments
112(a) rejections in the prior act is maintained with respect to “displaying, to a user, a graphical user interface (GUI) configured to display at least one of the first comprehensive breadth score, the second comprehensive breadth score, the competitive analysis report, or the technology breadth score” because the Applicant has no possession in the Original Disclosure for the newly added matter of “displaying” [three elements, namely] “the second comprehensive breadth score”, “the competitive analysis report”, and “the technology breadth score” on the same “GUI”, as broadly covered by the claimed expression “at least one” [thus up to three] “of the first comprehensive breadth score, the second comprehensive breadth score, the competitive analysis report, or the technology breadth score” as newly amended at independent Claim 1.
All other remaining 112(a) rejections in the prior act are withdrawn in view of Applicant amending independent Claim 1 to cancel the Examiner’s contested features.
Response to 101 Arguments / Amendments
Remarks 02/13/2026 p.9 ¶4 argues the amended claims render the 101 rejection moot.
Examiner fully considered the Applicant’s eligibility argument but respectfully disagrees finding it unpersuasive because the newly amended “vector representations” can be argued as abstract manipulations or representations of mathematical relationships that are expressed in words as tested per MPEP 2106.04(a)(2) I A. Thus, it can be argued that the ensuing “calculating, for the individual intellectual property assets of the plurality of intellectual property assets and utilizing the vector representations, a first breadth score based at least in part on a first word count score and a first commonness score for the respective portions of text included in the individual intellectual property assets” would also represent an abstract evaluation [MPEP 2106.04(A)(2) III ¶2] using such abstract mathematical relationships expressed in words [MPEP 2106.04(A)(2)I A].
Also, the fact that independent Claim 1 also recites “wherein the vector representations represent a lower dimensional version of the plurality of intellectual property assets that, when stored, requires less storage than storing the plurality of intellectual property assets” does not render the independent Claim 1 less abstract and eligible. For once, the Original Specification does not appear to provide a clear, deliberate and sufficient disclosure to show that Applicant had possession for the newly added matter of “wherein the vector representations represent a lower dimensional version of the plurality of intellectual property assets that, when stored, requires less storage than storing the plurality of intellectual property assets”. At most the Original Specification discloses at its ¶ [0066] 4th sentence: “Techniques to generate vectors representing IP assets may include vectorization techniques such as Doc2Vec, or other similar techniques”. Yet, the Applicant has not invented Doc2Vec nor is the Applicant alleging as much. Even if claimed, such Doc2Vec vectorization technique would still represent a conventional technique, as evidenced by at least the following publications, as relied upon based on MPEP 2106.05(d) I 2. (c):
- US 20220103586 A1 ¶ [0056] 1st-2nd sentences: Reduced vectors are generated by based on the sections of each report at operation 620. The text of each section may be converted to a reduced vector using conventional or other document embedding techniques, such as Doc2Vec; the resulting reduced vectors are each a numerical representation of a section of a risk report.
- US 20200192727 A1 ¶ [0343] ii. Vector embedding which produces a float-valued vector representing the content of the document in a special semantic space. This can be done using methods such as doc2vec neural model, or any other method capable to “compress” the unstructured or semi-structured text data into a space with the distance metric which preserves semantic relationships.
- US 20240104419 A1 mid-¶ [0045] data compression module 150 may utilize Word2Vec for computing a feature vector for each word in the database 130 (e.g., knowledge corpus) and/or the operational data, Doc2Vec for computing a feature vector for each operational log,
- US 20230032564 A1 ¶ [0100] Note that although vectors obtained by converting topic items included in each piece of the dialog content data for visualization with Doc2vec or the like have been clustered, for example, these vectors may be clustered after being dimensionally reduced in two dimensions using principal component analysis or the like.
- US 20200301672 A1 mid-¶ [0035] Word embedding vectors reduce the number of dimensions thus increasing the training speed of the model and reducing system memory requirements…. Reducing the numbers of required dimensions (e.g., features, variables, etc.) reduces the needed time and storage space
- US 20210034960 A1 ¶ [0028] 7th-8th, 9th 10th sentences: In various embodiments, CLP 150 utilizes word embedding techniques such as word2vec or doc2vec to produce vectors which denote similarities between words that share common contexts. Word embedding techniques create word-based vectors, …structured so that similar features are positioned in close proximity to each other in the vector space. Reducing the numbers of required dimensions (e.g., features, variables, etc.) reduces the needed time and storage space…
- US 11509540 B2 claim 1. A method for reducing storage space used in tracking behavior of a plurality of network endpoints by modeling the behavior with a behavior model…comprising: … generating, for each respective network endpoint, using each record of the respective dedicated queue originating from the respective network endpoint, a respective vector representing a respective behavior model, wherein the generating the respective vector further comprises: identifying a module of a plurality of modules that is idle, wherein the plurality of modules are programmed to generate the respective vectors representing the respective behavior models; commanding the idle module to generate the respective vector representing the respective behavior model by: encoding data of each respective record within the respective dedicated queue as a floating point value in the respective vector, wherein the encoding the data further comprises extracting the data from a field of the respective record, and concatenating the data into a string; and feeding the string into a Document to Vector (doc2vec) algorithm, thereby outputting the respective vector; storing each respective vector to a memory; and determining an anomalous behavior state for a network endpoint in the plurality of network endpoints by comparing the respective vector of the network endpoint to a normalcy threshold in a multidimensional space, wherein the plurality of records is of a first data size, wherein a sum of a data size of each respective behavior model is of a second data size, and wherein the second data size is two or more orders of magnitude smaller than the first data size.
According, there is a preponderance of legal and/or factual evidence that show that the Applicant’s amended and argued features are incapable to render the argued claims eligible.
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Response to prior art 35 USC 103 rejection
Remarks 02/13/2026 p.10 ¶3-p.11 argues the amended features are not taught by the prior art. The prior art has been considered but is moot in view of the new grounds of rejection.
Perkowski et al US 20160048936 A1 hereinafter Perkowski teaches the access and use of “vector representations” in calculations of claim scope or breath score as evidenced by at least:
Perkowski ¶ [0272] Fig.97 is a schematic representation illustrating the generation of a CSC-based Search Vector (CSC-SV) from the data and meta-data contained in the Claim Scope Concept Structure (CSCS) of each Claim Limitation Language String (CLLS) of a Claim being marked-up using the CSCSML of the present invention
Perkowski ¶ [0273] Fig.98 is a schematic representation illustrating the use of the set of generated CSC-based Search Vectors to search for and find a set of Prior Art References {PAR} containing prior art disclosure (e.g. searchable text and searchable graphics and/or images) discovered by the CSC-Based Search Vectors, and generating a set of Prior Art Search Records {PASR} for the retrieved Prior Art References (PAR);
Perkowski ¶ [0274] Fig.99 is a schematic representation illustrating the analyzing the Prior Art Reference Record (PARR) generated for each retrieved Prior Art Reference (PAR) discovered by the CSC-based Search Vectors, using the Claim Scope Concept (CSC) based Prior Art Reference Analysis Schema (or GUI), and the mapping of relevant prior art disclosure (discovered by the CSC-based Search Vectors) into corresponding CSC-indexed data fields in the Claim Scope Concept (CSC) based Prior Art Reference Analysis Schema (or GUI);
Perkowski ¶ [0298] Fig.118A through 118C, taken together, show an exemplary Excel-based cumulative prior art reference chart generated by system of Fig. 94, during or independent from the process of the present invention, in connection with patent claim analysis, search vector generation, prior art searching, prior art reference retrieval and analysis, and/or claim patentability analysis and charting operations;
Perkowski ¶ [0786] in Step P in Fig.41C, GUI screens in Figs.43BB,37BB are employed, so the user easily synthesize search vectors, based on patent claim prosecution history language linked to claim limitations, and then store these search vectors in the system database. Specifically, while logged into the patent analysis and charting system, use the GUIs and methods of the system to select scope concept definitions and patent claim prosecution history language linked to claim limitations and stored in the database system, to synthesize search vectors for use against prior art search engines. Automated methods for scope concept based search vector based searching, and prior art reference retrieval, tagging and mapping to corresponding scope concept query fields in scope concept based prior art reference analysis GUIs will be described in great technical detail below, after description of the workflow process of Mode 4.
Perkowski ¶ [1305] in STEP G in Fig.81B, the Applicant/Owner uses the Scope Concept Prior Art Search Vectors to conduct prior art searches against various patent databases (US, PCT, EPO, JPO, and other foreign searches), technical information databases, and the World Wide Web using Google Search Engines; such search efforts can be initiated through the Search Module of the PCW, or offline, as the case may be).
Perkowski ¶ [1325] As indicated in STEP AA in Fig.81G, Examiner logs into the PAIR/EFS Portal of the system, reviews and examines the presented Claims and corresponding Claim Scope Concept Profiles in view of…, (ii) the set of Scope Concept Profiles for the set of presented Claims, (iii) the Scope Concept Based Prior Art Search Vectors generated and used to conduct a prior art search against the subject matter of the Claims, (iv) the Scope Concept Prior Art Reference Profiles for each of the analyzed prior art references, and (v) the Claim Patentability Analysis Charts automatically generated by the Claim Patentability Analysis Module using the Claims and the disclosed Scope Concept Prior Art Reference Profiles; wherein documents (ii) through (v) are deemed to constitute scope concept instruments and as they are useful in determining the scope and boundaries of the pending Claim under examination.
Perkowski ¶ [1423] 2nd-9th sentences: As shown in Fig. 94, a parsing module (e.g. algorithm) is used to automatically parse the patent claims into claim limitation language strings (CLLS), typically using punctuation marks as guides to parsing boundaries. The parsed claim limitation language strings are assigned Claim Scope Concepts (CSC) as described hereinabove. This process is continued until a Set Of Claim Scope Concepts is formed for the Set Of Patent Claims under analysis. Then two paths of processing continue. Along the first path, the set of claim scope concepts for the patents claims under analysis are used to generate a Claim Scope Concept (CSC) Based Prior Art Reference Analysis Schema (and GUI), and then this Schema (and GUI) are provided to an automated prior art reference analyzer. Along the second path, a Claim Scope Concept (CSC) Based Search Vector Profile is generated for each patent claim under analysis, and then a Master Set of Claim Scope Concept (CSC) Based Search Vector Profiles are generated for the set of pending patent claims under analysis. A set of CSC-based Search Vectors are generated from the Master Set of Claim Scope Concept (CSC) Based Search Vector Profiles, and then supplied to the Search Engines which include patent and technical databases covering patents and technology around the world, in different countries and languages, with language translators where and as necessary, in a matter known in the language translation art. The Prior Art Search Engines deliver discovered prior art references containing prior art disclosure (e.g. searchable text and/or searchable graphics and/or images) specified by CSC-Based Search Vectors. Any given discovered prior art reference may contain prior art disclosure that is relevant to one or more or all of the claim limitation language strings (CLLS) of one or more Claims under analysis. Each discovered Prior Art Reference is then tagged with the CSC-based Search Vector that discovered the Prior Art Reference during the prior art search. The Retrieved prior art references tagged with the CSC-based search vectors are then provided to the automated prior art reference analyzer, operating accordance to the CSC-based Prior Art Reference Analysis Schema that was generated for the set of patent claims under analysis. The automated prior art reference analyzer processes (i) each CSC-based prior art reference (i.e. searchable document) tagged with the CSC-based search vectors that discovered the prior art reference during search, and (ii) the CSC-based Prior Art Reference Analysis Schema, described in Fig. 110, so as to generate a prior art reference CSC-based profile document shown in FIG. 121, for each prior art reference retrieved during the search.
Perkowski ¶ [1431] in STEP E of Fig.95B, the process involves using the set of generated CSCS-based Search Vectors {<CSCSID-SV>} to (i) search for and find a set of Prior Art References {PAR}. During this step of the search process, each Prior Art References that matches at least one of the CSCS-based Search Vectors is retrieved and stored for analysis. Also, a Prior Art Search Record (PASR) is generated for each retrieved Prior Art Reference, wherein each Prior Art Search Record (PASR) is created by tagging and/or linking the CSCS-based Search Vector used to retrieve a particular Prior Art Reference, with (i) the Prior Art Reference (PAR), (ii) the cited Prior Art Disclosure contained in the Prior Art Reference, and (iii) the Claim Scope Concept Structure Identifier (CSCSID) so as to create the Prior Art Search Record (PASR) that is stored in the system database for the corresponding CSCS-based Search Vector. This step of the process illustrated in FIG. 98, wherein a set of generated CSC-based Search Vectors are used to search for and find a set of Prior Art References {PAR} containing prior art disclosure (e.g. searchable text and searchable graphics and/or images) retrieved/discovered by the CSC-Based Search Vectors, and thereafter, a set of Prior Art Search Records {PASR} are generated for the retrieved Prior Art References (PAR)
Perkowski ¶ [1432] indicated in STEP F of Fig.95B, the process involves using the Prior Art Search Record (PASR) generated for each retrieved Prior Art Reference and the Claim Scope Concept Based Prior Art Reference Analysis Schema, to generate a set of Prior Art Reference Claim Scope Concept (CSC) Profile documents for the set of retrieved Prior Art References. During this step of the process, each generated Prior Art Reference Claim Scope Concept Structure Profile document comprises, for each Claim in the set of Claims, (i) a list of Claim Scope Concept Structures (CSCS), and (ii) an indication of whether or not each CSCS has been substantiated by Prior Art Disclosure contained in at least one Prior Art Reference that has been retrieved during the prior art search using the CSCS-based Search Vector linked to the CSCS. This step of the process is illustrated in FIG. 99, wherein the Claim Scope Concept (CSC) based Prior Art Reference Analysis Schema (or GUI) is used to analyze the prior art reference record generated for each retrieved prior art reference discovered by the CSC-based search vectors.
Perkowski mid-¶ [1734] (d) generating a set of CSCS-based search vectors based on the data and meta-data contained in the Claim Scope Concept Structure (CSCS) of each Claim Limitation Language String contained in each Claim; (e) using the set of CSCS-based Search Vectors to search for and find a set of Prior Art References, each matching at least one of the CSCS-based Search Vectors, and a generate set of Prior Art Search Records for the retrieved Prior Art References, wherein each prior art search record is created by linking to or tagging the CSCS-based Search Vector used to retrieve a particular prior art reference, with the Prior Art Reference, cited Prior Art Disclosure and the Claim Scope Concept Structure Identifier (CSCSID), to create the Prior Art Search Record that is stored in the system database for the corresponding CSCS-based Search Vector; (f) using the Prior Art Search Record (PASR) generated for each retrieved prior art reference, and the Claim Scope Concept Based Prior Art Reference Analysis Schema, to generate a set of Prior Art Reference Claim Scope Concept Profile Documents for the set of retrieved Prior Art References, wherein each Prior Art Reference Claim Scope Concept Structure Profile document comprises, for each Claim in the set of Claims, a list of Claim Scope Concept Structures (CSCS), and indication of whether or not each CSCS has been substantiated by prior art disclosure from at least one prior art reference retrieved from the search, and indexed with the CSCS-based search vector that was used to retrieve the prior art reference containing the prior art disclosure; (g) optionally, organizing the Claim Scope Concept Structures (CSCS) in the Prior Art Reference Claim Scope Concept Structure Profile documents by grouping the Claim Scope Concept Structures (CSCS) having the same or similar Inventive Feature Phrases (IFP), or at least a predetermined number of common IFP words, so that a human reviewer or analyzer can see all Claim Scope Concept Structures (CSCSs); (h) automatically processing the set of Prior Art Reference Claim Scope Concept Profile documents to analyze the retrieved set of Prior Art References, and generate Claim Patentability Analyses based on the analyzed retrieved Prior Art References, and then generate Claim Patentability Analysis Charts according to the present invention; (i) ranking the Prior Art References retrieved during the search results according to which Prior Art References have the Maximum Number Of Claim Scope Concepts (CSC) Substantiated By Prior Art Reference Disclosure (Mapped By The CSC-Based Search Vectors), Then Order Prior Art Reference Analysis by a human subject matter expert according to The Highest Priority Of CSC-Ranking.
Yet, Perkowski disclosure of vectors presentations does not go so far to explicitly recite, does not explicitly recite: “wherein the vector representations represent a lower dimensional version of the plurality of intellectual property assets that, when stored, requires less storage than storing the plurality of intellectual property assets”, as claimed [bolded emphasis added].
However, Zhang et al, US 20200301672 A1 hereinafter Zhang, in analogous art of vector representation in a data set teaches or at least suggests:
- “wherein the vector representations represent a lower dimensional version of the plurality of intellectual property assets that, when stored, requires less storage than storing the plurality of intellectual property assets”, (Zhang mid-¶ [0035] Word embedding vectors reduce [or lowers] the number of dimensions thus increasing the training speed of the model and reducing system memory requirements…. program 150 utilizes dimension reducing techniques, such as feature extraction, low-dimensional embedding, and kernelling, to reduce the number of dimensions required to represent the training data and features. Reducing the numbers of required dimensions (e.g., features, variables, etc.) reduces the needed time and storage space….)
Thus, the prior art teaches or at least suggests the newly amended “vector representations” disclosure and hence the prior argument is unpersuasive.
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Claim Rejections - 35 USC § 112
The following is a quotation 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 35 U.S.C. 112 (pre-AIA ), first paragraph:
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 1-3 and 5-9 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 claim(s) contains 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.
Claim 1 has been previously amended to include the newly added matter of:
- “displaying, to a user, a graphical user interface (GUI) configured to display at least one of the first comprehensive breadth score, the second comprehensive breadth score, the competitive analysis report, or the technology breadth score” [bolded emphasis added].
-> Original Specification ¶ [0040] 2nd sentence merely recites at a high level: “The analysis report(s) may include, for example”…“comprehensive breadth score results”…
At no point does the Original Disclosure provide clear, deliberate and sufficient support to show that Applicant had possession for the newly added matter of “at least one” [thus up to three] “of the first comprehensive breadth score, the second comprehensive breadth score, the competitive analysis report, or the technology breadth score” as amended at Claim 1.
Claim 1 has been also newly amended, with the amendment dated 02/13/2026 to recite:
- “wherein the vector representations represent a lower dimensional version of the plurality of intellectual property assets that, when stored, requires less storage than storing the plurality of intellectual property assets”. [bolded emphasis added]. The Original Specification does not appear to provide clear, deliberate and sufficient disclosure to show that Applicant had possession for the newly added matter of “wherein the vector representations represent a lower dimensional version of the plurality of intellectual property assets that, when stored, requires less storage than storing the plurality of intellectual property assets”. At most the Original Specification ¶ [0066] 4th sentence recites: “Techniques to generate vectors representing IP assets may include vectorization techniques such as Doc2Vec, or other similar techniques” which does not provide clear, deliberate and sufficient support that Applicant had possession for said newly added matter.
Claims 2,3 and 5-9 are dependent and rejected based on rejected parent Claim 1.
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Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(B) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-3 and 5-9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 is independent and has been amended to recite, among others:
“generating vector representations of a plurality of intellectual property assets associated with an entity, individual intellectual property assets including respective portions of text”… etc.
Claim 1 is rendered vague and indefinite because it is unclear if “individual intellectual property assets” as subsequently recited in said limitation antecedently relates back to “intellectual property assets” as previously recited in said limitation.
Claim 1 is recommended to be amended to recite, among others and as an example only:
generating vector representations of a plurality of intellectual property assets associated with an entity, the plurality of individual intellectual property assets including respective portions of text”… etc. etc.
Claims 2,3 and 5-9 are dependent and rejected based on rejected parent Claim 1.
Clarification and/or correction is/are required.
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 1-3 and 5-9, are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea, here abstract idea) without significantly more. Examiner points to MPEP 2106.04(a) last ¶: to submit that …examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible, if a claim limitation(s) is determined to fall within multiple groupings… Here, the claims still recite, describe or set forth the abstract intellectual-property analysis, as read in light of the Title of the Original Disclosure and the background of the Original Specification at ¶ [0001]. Such intellectual-property analysis can be viewed as part of abstract managing commercial, legal or business relationships and/or fundamental economic principles or practices, integral to the abstract “Certain Methods of Organizing Human Activities” grouping (MPEP 2106.04(a)(2) II). Specifically, here, the claims recite the fundamental “individual intellectual property assets including respective portions of text”, (independent Claim 1), “technology area” (Claims 1,8) “market area” (dependent Claim 6), “first intellectual property asset that is filed; second intellectual property asset that is granted; third intellectual property asset that is expired; fourth intellectual property asset that is abandoned; fifth intellectual property asset that is changed” (dependent Claim 5). Such abstract managing commercial, legal, business relationships and/or fundamental economic principles / practices are further formulated and/or implemented by equally abstract “Mental Processes” through mental and/or computer-aided observation, evaluation, judgment, opinion (MPEP 2106.04(a)(2) III) using equally abstract mathematical relationships expressed in words (MPEP 2106.04(a)(2) I).
Specifically, here, given the claims’ breadth, nothing would have precluded one of ordinary skills in the art, to use his or her cognitive capabilities as substitutes for the recited “one or more processors” or even with the aid of such “processors” (Claims 1) to:
evaluate, using equally abstract mathematical relationships expressed in words the abstract “calculating, for the individual intellectual property assets of the plurality of intellectual property assets and utilizing the vector representations, a first breadth score based at least in part on first word count score and a first commonness score for the respective portions of text included in the individual intellectual property assets”; (independent Claims 1).
Also here, given the claims’ breadth, nothing would have precluded one of ordinary skills in the art, to use his or her cognitive capabilities to:
evaluate, using equally abstract mathematical relationships expressed in words the abstract “calculating a weighted score for the individual intellectual property assets based at least in part on multiplying the first breadth score by a first weight the first weight, the first weight being based at least in part on respective breadth scores for the individual intellectual property assets” (independent Claim 1).
Also here, given the claims’ breadth, nothing would have precluded one of ordinary skills in the art to use his/her cognitive capabilities with or without computer aids to:
evaluate, using abstract mathematical relationships expressed in words the abstract “calculating a first comprehensive technology breadth score associated with a first period of time for the plurality of intellectual property assets by calculating an average of weighted scores of the individual intellectual property assets” (independent Claim 1) for
judgement or “determining a technology area associated with at least a subset of intellectual property assets of the plurality of intellectual property assets”(Claim 1) and subsequent
evaluation of “calculating, for the subset of intellectual property assets that are associated with the technology area and utilizing the vector representations, a subset breadth score based at least in part on a second word count score and a second commonness score for the respective portions of text included in the subset of intellectual property assets that are associated with the technology area” (Claim 1); “calculating a subset weighted score for the subset of intellectual property assets associated with the technology area by multiplying individual subset breadth scores by second weight based at least in part on value of a respective subset breadth score for individual subset intellectual property assets that are associated with the technology area” (Claim 1); “calculating a technology breadth score for the subset of intellectual property assets that are associated with the technology area by calculating an average of individual subset weighted scores”; (Claim 1), “determining at least a second comprehensive breadth score for the plurality of intellectual property assets associated with a second period of time, the second period of time being after the first period of time and after the plurality of intellectual property assets have issued” (Claim 1), to convey a subsequent judgement, opinion or “competitive analysis report for the entity with the second comprehensive breadth score” (Claim 1) for subsequent evaluation or “categorizing the individual intellectual property assets based at least in part on their respective weighted scores into distinct categories” (Claim 1) and observation “or visualization of the distinct categories” (Claim 1).
Additional, narrowing examples of abstract evaluation are recited as “determining at least a second technology breadth score for the subset of intellectual property assets associated with the second period of time” (dependent Claim 2), “calculating, for the subset of intellectual property assets that are associated with the market area, a subset breadth score based at least in part on a third word count score and a third commonness score for the respective portions of text included in the subset of intellectual property assets that are associated with the market area” (dependent Claim 6); “calculating a subset weighted score for the subset of intellectual property assets that are associated with the market area by multiplying the individual subset breadth scores by a third weight based at least in part on a value of the respective subset breadth score for individual subset intellectual property assets that are associated with the market area” (dependent Claim 6); “calculating a market breadth score for the subset of intellectual property assets that are associated with the market area by calculating an average of the individual subset weighted scores” (dependent Claim 6); “calculating, for the second subset of intellectual property assets that are associated with the second entity, a subset breadth score based at least in part on a third word count score and a third commonness score for the respective portions of text included in the second subset of intellectual property assets that are associated with the second entity” (dependent Claim 8); “calculating a subset weighted score for the second subset of intellectual property assets that are associated with the second entity by multiplying the individual subset breadth scores by a third weight based at least in part on a value of a respective subset breadth score for individual second subset intellectual property assets that are associated with the second entity” (dependent Claim 8); “calculating a subset comprehensive breadth score for the second subset of intellectual property assets that are associated with the second entity by calculating an average of the individual subset weighted scores” (dependent Claim 8), and
Finally computer-aided observation with his or her eyes of computer aided “display” of “at least one of the first comprehensive breadth score, the second comprehensive breadth score, the competitive analysis report, or the technology breadth score”, “presenting” “a visualization of the distinct categories” (independent Claim 1), “display the first comprehensive breadth score and the second comprehensive breadth score” (dependents Claims 9), “display first market breadth score and the second market breadth score” (dependent Claim 7), “present metric data associated with the first comprehensive breadth score” (dependent Claim 3).
Examiner consolidates such findings and rationales by pointing to MPEP 2106.04(a)(2) III A ¶8 which cites Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016) to state that the combination of collecting information, analyzing it, and displaying certain results of collection and analysis, are directed to the abstract idea.
Based on such finding, Examiner asserts that here, as in “Electric Power Group” as cited by MPEP 2106.04(a)(2) III A, ¶8, the collecting of “individual intellectual property assets” (independent Claim 1), and the subsequent “calculating” and “determining” or analyzing of such “intellectual property assets” (independent Claim 1) for ultimately “displaying” “to a user” “the comprehensive breadth score or/ technology breadth score” (independent Claim 1), “presenting” “a visualization of the distinct categories” (independent Claim 1), “displaying the first comprehensive /subset/ breadth score and second comprehensive/subset/breadth score” (dependent Claim 9), “displaying first market breadth score and the second market breadth score” (dependent Claim 7), “present metric data associated with” “first comprehensive breadth score” (dependent Claim 3), would still set forth or describe the abstract exception.
As per recitation of “receiving a new user input via the GUI and causing the GUI to present metric data associated with the comprehensive breadth score in response to the new user input” (dependent Claim 3), and “wherein the GUI is dynamically updated based at least in part on receiving new user input” (independent Claim 1), the Examiner points to MPEP 2106.04(a)(2) II ¶6, 4th sentence which states that “certain activity between a person and a computer (for example a method of anonymous loan shopping that a person conducts using a mobile phone) may fall within the "certain methods of organizing human activity" grouping”. Indeed, according to MPEP 2106.04(a)(2) II C ii1 considering historical usage information while inputting data, still falls in within the managing of user interactions of the abstract “Certain Methods of Organizing Human Activities” grouping. The same MPEP 2106.04(a)(2) II C ¶7 states that acquiring content from an information source, controlling the timing of the display of acquired content, displaying the content, and acquiring an updated version of the previously-acquired content when the information source updates its content2 falls within the abstract “Certain Methods of Organizing Human Activities” grouping. Thus, the Examiner submits that at most, recitation of “receiving the new user input on the display and causing the GUI to present metric data associated with the comprehensive breadth score in response to the user input” (dependent Claim 3), and similarly “wherein the GUI is dynamically updated based at least in part on receiving new user input”; “presenting, via the GUI, a visualization of the distinct categories” (independent Claim 1) would not preclude the claims to recite, describe or set forth the abstract exception. In a similar vein, MPEP 2106.04(a)(2) III C. states: #1. Performing a mental process on a generic computer, #2. Performing a mental process in a computer environment, #3. Using a computer as a tool to perform a mental process does not preclude the claims from reciting describing or setting forth the abstract exception.
Based on the preponderance of legal evidence demonstrated above, Examiner submits that recitation of “graphical user interface (GUI)” to “receive” “input” and “display, present” scores at Claims 1,3,7,9 would not preclude the claims from reciting, describing or setting forth the abstract exception. Such computer-based elements will be more granularly investigated in the subsequent step below. For now, it is clear that here, there is a preponderance of legal evidence showing that the claims still recite, describe or set forth the abstract exception. Step 2A prong 1.
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This judicial exception is not integrated into a practical application because per Step 2A prong 2, since the individual or combination of the additional, computer-based elements is/are found to merely apply the already abstract idea MPEP 2106.05(f) and/or further narrow it to a technological environment or field of use MPEP 2106.05(h). To elaborate, here, the instruct[ed] “one or more processors” and storage of “vector representations” at independent Claim 1, and “graphical user interface (GUI)” of Claims 1,3 were tested above and argued as computer aids which would not preclude the claims from reciting, describing the abstract exception. Examiner now submits, in the arguendo, that even if now more granularly tested beyond the realm of computer aids [Step 2A prong one], and as additional computer-based elements, [Step 2A prong two], they would still represent, under MPEP 2106.05(f)(2), mere tools to apply the abstract idea and its underlining algorithm and to perform economic tasks or other tasks to receive, store and transmit data, as already identified above. according to MPEP 2106.05(f)(2) such computerized functionalities would represent mere invocation of computer or machinery that would not integrate the abstract exception into a practical application. In such a case, when the instruct[ed] “one or more processors”, storage, and “graphical user interface GUI”, would be tested as additional, computer-based elements, they would merely execute or be involved in the execution of above combination of fundamental or economic practices, mental processes and associated weighted mathematical relationships. Yet MPEP 2106.05(f)(2)(i)3 is clear that: a business method and its underlining mathematical algorithm applied on a general-purpose computer represents mere invocation of computers or machinery as tools to perform the abstract process, and thus does not integrate the abstract exception into a practical application. Thus, Examiner reasons that here, the capabilities of instruct[ed] “processors”, when argued as additional computer-based elements, to similarly perform the fundamental business or economic functions along with their algorithms, as identified above, do also represent invocation of computer capabilities as tools to apply the abstract idea, without integrating it into a practical application. Also, MPEP 2106.05(f)(2) ¶1, states that use of a computer or other machinery for economic or other tasks (to receive, store, or transmit data)4 does not integrate the abstract idea into a practical application. Step 2A prong two. Thus, Examiner reasons that here, as in the MPEP 2106.05(f) examples above, the capabilities of the instruct[ed]” “processors of Claim 1, in “accessing a database storing vector representations”, would similarly represent mere applications of the abstract concepts by computer related elements, which do not integrate the abstract idea into a practical application.
Further, MPEP 2106.05(f)(2) states that monitoring audit log data that is executed on a general-purpose computer5, and requiring use of software to tailor information and provide it to the user on a generic computer6 are examples that do no more than merely invoke computers or machinery as a tool to perform an existing process, and thus do not integrate the abstract idea into a practical application. Here, the Examiner similarly reasons that the capabilities of the additional, computer-based elements such as “graphical user interface (GUI)” to “display the comprehensive breadth score, the competitive analysis report or the technology breadth score” and “wherein the GUI is dynamically updated based at least in part on receiving new user input”
[of arguably limited patentable weight7], and similar recitations of “presenting, via the GUI, a visualization of the distinct categories” (independent Claim 1), “display the first comprehensive breadth score and the second comprehensive breadth score” (dependent Claim 9), “display first market breadth score and the second market breadth score” (dependent Claim 7), “present” “metric data associated with the comprehensive breadth score” (dependent Claim 3), would be comparable to the above examples of monitoring data and tailoring information using computer tools, and thus should similarly be construed as merely applying the aforementioned abstract concepts without integrating the abstract exception into a practical idea. Step 2A prong two.
In fact, MPEP 2106.05 (f)(2) cites “Fairwarning Ip, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 U.S.P.Q.2d 1293 (Fed. Cir. 2016), where FairWarning contend[ed] that its system allowed for the compilation and combination of [*1097] these disparate information sources and that the patented method "made it possible to generate a full picture of a user's activity, identity, frequency of activity, and the like in a computer environment." Id. at 10. Yet, Federal Circuit responded: “the mere combination of data sources, however, does not make the claims patent eligible. As we have explained, "merely selecting information, by content or source, for collection, analysis, and [announcement] does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from § 101 undergirds the information-based category of abstract ideas." Elec.Power,830 F.3d 1350, [2016 BL 247416], 2016 WL 4073318, at *4. It then follows that, the analogous selecting or “accessing a database storing vector representations”, (independent Claim 1) for collection and subsequent analysis expressed here through repeated “calculating” and “determining”, and then for announcement expressed here by display or present[ation] “via the GUI” as recited at independent Claim 1, and similarly at dependent Claims 3,7,9, would also not differentiate the claims from the information-based category of abstract ideas, in a manner not meaningfully different than FairWarning and Elec. Power supra.
Additionally, or alternatively, the Examiner also points to MPEP 2106.05(h) which states that: even the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the technological field or field of use does not integrate the abstract idea into a practical application. Thus, even if Applicant’s would attempt to narrow the abstract idea to a field of use or technological environment, such attempts would not integrate the abstract idea into a practical appclaition. Step 2A prong two.
Examiner thus submits that there is preponderance of legal evidence showing the additional, computer-based elements do not integrate the abstract exception into a practical application.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as shown above, the additional computer-based elements merely apply the already recited abstract idea [MPEP 2106.05(f)] or narrow it to a field of use or technological environment [MPEP 2106.05(h)]. Specifically Examiner follows the guidelines of MPEP 2106.05(d) II 2nd bullet point and caries over the conclusions reached on the MPEP 2106.05(f) and/or (h) tests above to Step 2B, and submits that for the same reasons articulated above, said computer-based additional elements also do not provide significantly when considering MPEP 2106.05(f) and/or (h) as sufficient option(s) for evidence, without the need to rely on the well-understood, routine and conventional test of MPEP 2106.05(d). This is because MPEP 210.05(d) is merely one of the many options of MPEP 2106.05(a)-(h) to test whether the additional elements provide significantly more. In this respect, MPEP 2106.05I ¶1 states that the conventional consideration overlaps with the mere instructions to apply an exception consideration test, (MPEP 2106.05(f)), the insignificant extra-solution activity consideration test (MPEP 2106.05(g)) etc. In this instant case, Examiner found that per MPEP 2106.05(f) and/or (h) tests, no additional computer-based elements provide significantly more. Yet, assuming arguendo, just for the sake of argument, that further evidence would be required to demonstrate conventionality of any asserted additional, computer-based elements, the Examiner would further rely on MPEP 2106.05(d) guidelines to demonstrate that said additional elements are also well-understood, routine, conventional. In such case, the Examiner would rely as evidence on case law as cited by MPEP 2106.05(d) II and/or the Applicant’s own Specification as instructed by MPEP 2106.05(d) I 2.
For once MPEP 2106.05(d) II I, ii, iii, iv shows conventionality of similar computer elements performing comparable functions of: electronic recordkeeping8, storing and retrieving info in memory9, recording customer’s order10, performing repetitive calculations11 as well as receiving or transmitting data including utilizing intermediary computer to forward info12 which do not provide significantly more. In this instance case, receiving or transmitting data is reflected here by the capabilities of the instructed processors of independent Claims 1 in “accessing a database storing the vector representations”. Also utilizing intermediary computer to forward information is reflected here by the recitation of “receiving a new user input on the display and causing the GUI to present metric data associated with the comprehensive breadth score in response to the new user input” at dependent Claim 3. Finally, the repeated calculations are repeatedly recited throughout Claims 1,6,8. Thus, the Examiner would then reason that the analogous, computerized capabilities of the “instruct[ed]” “processors”, storage, and “GUI” would similarly not provide significantly more.
If further necessary, the Examiner would also point as evidence to high level of generality of the additional elements at as instructed by MPEP 2106.05(d) I 2:
Original Specification ¶ [0076] reciting at high level of generality “As used herein, a processor, such as processor(s) 108 and/or 116, may include multiple processors and/or a processor having multiple cores. Further, the processors may comprise one or more cores of different types. For example, the processors may include application processor units, graphic processing units, and so forth. In one implementation, the processor may comprise a microcontroller and/or a microprocessor. The processor(s) 108 and/or 116 may include a graphics processing unit (GPU), a microprocessor, a digital signal processor or other processing units or components known in the art. Alternatively, or in addition, the functionally described herein can be performed, at least in part, by one or more hardware logic components. For example, and without limitation, illustrative types of hardware logic components that can be used include field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), etc. Additionally, each of the processor(s) 108 and/or 116 may possess its own local memory, which also may store program components, program data, and/or one or more operating systems”.
Original Specification ¶ [0077] at high level: computer-readable media 112 and/or 120 may include volatile and nonvolatile memory, removable and non-removable media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program component, or other data. Such computer-readable media 112 and/or 120 includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, RAID storage systems, or any other medium which can be used to store the desired information and which can be accessed by a computing device. The computer-readable media 112 and/or 120 may be implemented as computer-readable storage media ("CRSM"), which may be any available physical media accessible by the processor(s) 108 and/or 116 to execute instructions stored on the computer- readable media 112 and/or 120. In one basic implementation, CRSM may include random access memory ("RAM") and Flash memory. In other implementations, CRSM may include, but is not limited to, read-only memory ("ROM"), electrically erasable programmable read- only memory ("EEPROM"), or any other tangible medium which can be used to store the desired information and which can be accessed by the processor(s)”
Original Specification ¶ [00121] reciting at a high level of generality the combination among the additional, computer-based elements: “The processes described herein are illustrated as collections of blocks in logical flow diagrams, which represent a sequence of operations, some or all of which may be implemented in hardware, software or a combination thereof”.
Original Specification ¶ [00252] reciting at high level of generality: “While the foregoing invention is described with respect to the specific examples, it is to be understood that the scope of the invention is not limited to these specific examples. Since other modifications and changes varied to fit particular operating requirements and environments will be apparent to those skilled in the art, the invention is not considered limited to the example chosen for purposes of disclosure, and covers all changes and modifications which do not constitute departures from the true spirit and scope of this invention”.
Original Specification ¶ [0253] reciting at high level of generality: “Although the application describes embodiments having specific structural features and/or methodological acts, it is to be understood that the claims are not necessarily limited to the specific features or acts described. Rather, the specific features and acts are merely illustrative some embodiments that fall within the scope of the claims”.
As per newly amended “wherein the vector representations represent a lower dimensional version of the plurality of intellectual property assets that, when stored, requires less storage than storing the plurality of intellectual property assets” recited at independent Claim 1, the Examiner points to the high level of generality of Original Specification ¶ [0066] 4th sentence: “Techniques to generate vectors representing IP assets may include vectorization techniques such as Doc2Vec, or other similar techniques”.. It is clear that Applicant has not invented Doc2Vec nor is the Applicant alleging as much. Even if claimed, such Doc2Vec vectorization technique would still represent a conventional technique, as evidenced by at least the following publications, as relied upon based on MPEP 2106.05(d) I 2. (c):
- US 20220103586 A1 ¶ [0056] 1st-2nd sentences: Reduced vectors are generated by based on the sections of each report at operation 620. The text of each section may be converted to a reduced vector using conventional or other document embedding techniques, such as Doc2Vec; the resulting reduced vectors are each a numerical representation of a section of a risk report.
- US 20200192727 A1 ¶ [0343] ii. Vector embedding which produces a float-valued vector representing the content of the document in a special semantic space. This can be done using methods such as doc2vec neural model, or any other method capable to “compress” the unstructured or semi-structured text data into a space with the distance metric which preserves semantic relationships.
- US 20240104419 A1 mid-¶ [0045] data compression module 150 may utilize Word2Vec for computing a feature vector for each word in the database 130 (e.g., knowledge corpus) and/or the operational data, Doc2Vec for computing a feature vector for each operational log,
- US 20230032564 A1 ¶ [0100] Note that although vectors obtained by converting topic items included in each piece of the dialog content data for visualization with Doc2vec or the like have been clustered, for example, these vectors may be clustered after being dimensionally reduced in two dimensions using principal component analysis or the like.
- US 20200301672 A1 mid-¶ [0035] Word embedding vectors reduce the number of dimensions thus increasing the training speed of the model and reducing system memory requirements…. Reducing the numbers of required dimensions (e.g., features, variables, etc.) reduces the needed time and storage space
- US 20210034960 A1 ¶ [0028] 7th-8th, 9th 10th sentences: In various embodiments, CLP 150 utilizes word embedding techniques such as word2vec or doc2vec to produce vectors which denote similarities between words that share common contexts. Word embedding techniques create word-based vectors, …structured so that similar features are positioned in close proximity to each other in the vector space. Reducing the numbers of required dimensions (e.g., features, variables, etc.) reduces the needed time and storage space…
- US 11509540 B2 claim 1. A method for reducing storage space used in tracking behavior of a plurality of network endpoints by modeling the behavior with a behavior model, the method comprising:
… generating, for each respective network endpoint, using each record of the respective dedicated queue originating from the respective network endpoint, a respective vector representing a respective behavior model, wherein the generating the respective vector further comprises: identifying a module of a plurality of modules that is idle, wherein the plurality of modules are programmed to generate the respective vectors representing the respective behavior models; commanding the idle module to generate the respective vector representing the respective behavior model by: encoding data of each respective record within the respective dedicated queue as a floating point value in the respective vector, wherein the encoding the data further comprises extracting the data from a field of the respective record, and concatenating the data into a string; and feeding the string into a Document to Vector (doc2vec) algorithm, thereby outputting the respective vector; storing each respective vector to a memory; and determining an anomalous behavior state for a network endpoint in the plurality of network endpoints by comparing the respective vector of the network endpoint to a normalcy threshold in a multidimensional space, wherein the plurality of records is of a first data size, wherein a sum of a data size of each respective behavior model is of a second data size, and wherein the second data size is two or more orders of magnitude smaller than the first data size.
In conclusion, Claims 1-3 and 5-9 although directed to statutory categories (“system” or machine) they still recite or set forth the abstract idea (Step 2A prong one), with their additional, computer based elements not integrating the abstract idea into a practical application (Step 2A prong two) or providing significantly more than the abstract idea itself (Step 2B).
Thus, Claims 1-3 and 5-9 are patent ineligible.
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Rejections under 35 § U.S.C. 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 of this title, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3 and 5-7 are rejected under 35 U.S.C. 103 as being unpatentable over applied to claim, and in view of: Daniel Crouse et al, US 20180253486 A1 hereinafter Crouse, in view of
Edmund US 20180253810 A1 hereinafter Edmund, in view of
Perkowski et al US 20160048936 A1 hereinafter Perkowski, in view of
Zhang et al US 20200301672 A1 hereinafter Zhang and in further view of
Chan; Alex H et al, US 20190079979 A1 hereinafter Chan. As per,
Claim 1 Crouse teaches “A system comprising: one or more processors; and one or more computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processor to perform operations comprising”
(Crouse ¶ [0059] 3rd sentence, ¶ [0132]-¶ [0135], ¶ [0165], ¶ [0178]):
- “”,
- “accessing a database ;
(Crouse ¶ [0015] 2nd sentence: documents stored in data repositories accessed automatically by computing devices and analyzed based on rule sets. ¶ [0021] 2nd sentence, ¶ [0038] 2nd sentence, ¶ [0061] 1st sentence, ¶ [0084] 3rd sentence ¶ [0100] 3rd sentence, ¶ [0108] 3rd sentence, ¶ [0126] 3rd sentence: noting several examples showing the documents are received from data repositories, such as data repositories 102. ¶ [0137] 4th sentence: The collection of patents or patent applications may be defined by, a portfolio of a patent owner. ¶ [0023] 4rd-5th sentences: Data filtering 104 filter by patent type to keep utility patents while excluding design and plant patents. Data filtering 104 may also filter documents by author, inventor, assignee, technical field, classification etc. ¶ [0024] 2nd sentence: pre-processing 106 include stripping out punctuation, removing stop words 108, converting acronyms and abbreviations 110 to full words, stemming, and/or removing duplicate words. ¶ [0062] 2nd-3rd sentences: Each document in the data repository may be associated with a unique document identification number. A patent document which may include application, publication, patent number, and/or combination of info associated with the patent document that uniquely identify the patent document (such as combination of name of inventor, filing date)
- “calculating, for the individual intellectual property assets of the plurality of intellectual property assets , a first breadth score based at least in part on a first word count score” (Crouse ¶ [0016] 1st-4th sentence documents may be analyzed to determine (calculate) comparative breadth scores associated with breadths of the documents. In some examples, breadth of document portions may be analyzed on consideration of word count and commonality of words. Thus, the number of unique words and the frequency with which those words appear in other document portions (e.g. document portions of other documents) are basis for automatically assigning a breadth score to a given document portion. For instance, for a given document portion of a given document, word count is compared to word count of other document portions in same analysis) “and a first commonness score for the respective portions of text included in the individual intellectual property assets” (Crouse ¶ [0016] 5th sentence: similarly commonness score is determined for the given document portion based on the commonality of words in that document portion as compared to the commonality of words in other document portions from same analysis);
- “calculating a weighted score for the individual intellectual property assets based at least in part on multiplying the first breadth score by a first weight, the first weight being based at least in part on respective breadth scores for the individual intellectual property assets”;
(Crouse ¶ [0021] Fig.1 illustrates an example analysis pipeline 100 for automatically analyzing and presenting breadth information derived from multiple documents. ¶ [0028] 2nd-3rd sentences: Specific techniques for determining breadth score are discussed below. Some documents have multiple portions that are score. For example, per ¶ [0019] 4th sentence: the comparative breadth score may be multiplied by a first weight to determine a weighted breadth score. ¶ [0097] 4th-5th sentences: where overall breadth score is based on the score of multiple document portions (represented as a weighted composite of the broadest or individual component scores such as broadest), calculation 722 compares that score or scores to the score or scores of corresponding multiple document portions of other documents within the analysis. For example, in a context where the documents are patents and the portions are claims, calculation 722 compare the breadth score of the broadest claim in a patent to the breadth score of the broadest claims in all patents within the landscape)
- “calculating a first comprehensive breadth score ”; (Crouse ¶ [0019] 3rd-5th sentences: for a given document, the comprehensive score can include weighted average (and/or weighted mean, weighted mode, weighted lowest score, weighted highest score, etc.) of the comparative breadth score, the comparative portion count score, and/or the comparative differentiation score. For instance, the comparative breadth score may be multiplied by 1st weight to determine a weighted breadth score, the comparative portion count score may be multiplied by 2nd weight to determine a weighted portion score, and the comparative differentiation score may be multiplied by a 3rd weight to determine a weighted differentiation score. The comprehensive score for the document can then be determined based on an average (and/or mean, mode, lowest score, highest score, etc.) of the weighted breadth score, the weighted portion count score, and weighted differentiation score);
- “determining a technology area associated with at least a subset of intellectual property assets of the plurality of intellectual property assets” (Crouse ¶ [0061] 2nd, 4th sentences: a collection of patents and/or applications gathered from data repository limited to a technology area. the collection may be obtained based on classification codes, such as USPTO classes and subclasses, or IPC. ¶ [0024] 14th sentence: data filtering 104 restricts the documents to a specific technical area. ¶ [0066] 8th-9th sentences: If a given data repository is known to be limited to containing only documents of a certain type, then all documents obtained from that data repository may be assumed to be of the specified type. For example, a document obtained from a data repository that only contains academic papers on biotechnology may be identified as an academic paper on biotechnology by virtue of coming from this specific data repository);
- “calculating, for the subset of intellectual property assets that are associated with the technology area, (Crouse [0031] analysis captures the idea of comparing apples to apples when calculating comprehensive breadth scores. For instance, comparison of breadth of a biotechnology patent to the breadth of a mechanical patent is less meaningful than comparing breadth of one software patent to the breadth another software patent. Because the documents are given overall breadth scores with respect to other documents in same corpus, those overall breadth scores may be utilized to determine the comprehensive breadth scores for each of the documents.
Crouse ¶ [0049] 2nd-7th sentences: calculation 302 may determine the number of unique words in the portion determined to have broadest overall breadth score. For each additional document portion, the calculation 302 determine number of unique words in the portion that are not included in the portion having broadest overall breadth score. In other example, the calculation determine the number of unique words that are included in that particular portion and not included in any other portion. In some instances, the number of unique words associated with each portion is then expressed as % of the unique words within the corpus of words in the relevant documents. For example, if corpus of words in the relevant documents includes 10,000 unique words, and a given document portion (e.g. independent claim) includes 20 unique words within the corpus of 10,000 unique words, then the % for the given document portion is 0.002%. If a second document portion (e.g., independent claim) also includes 20 unique words that are both within the corpus of 10,000 unique words and exclusive of the words in the first (or any other previously processed) document portion, then the percentage for the second document portion is also 0.002%.
Crouse Fig.7 and ¶ [0094] at 718, breadth scores for the document portions are calculated using the word count ratios and the commonness score ratios. For instance, the breadth scores may be calculated by taking [Symbol font/0xD6] of the sum of the square of the word count ratio (wcr) and square of the commonness score ratio (csr) for the individual ones of the processed document portions. In some instances, the relative weights of the word count ratio and the commonness score may be normalized. One technique for normalization is to set highest respective values for both word count ratio and commonness score ratio to 100. If highest word count ratio is h−wcr, then all of wcr for the corpus will be multiplied by 100/h−wcr. Similar, normalization may be performed for the commonness score ratio using the highest commonness score ratio (h−csr). Of course, normalization values other than 100 may be used, such as 1000, 500, 50, 10, or the like. Both are numbers, but the relative effect on a breadth score may not directly correspond to the respective numerical values. For example, a word count ratio of 10 may have more or less impact on ultimate breadth than a commonness score ratio of 10. However, without normalization both contribute equally to the breadth score. As such, the word count ratio may be weighted by 1st normalization value K (e.g. 100/h−wcr) and the commonness score ratio may be weighted by a 2nd normalization value L (e.g. 100/h−csr). When written in equation: Breadth Score=K(wcr2)+L(csr2). Thus, each document portion may be assigned its own breadth score. The breadth scores may be thought of as measuring the breadth of the document portions because the breadth scores are based on measures of word count and word commonness. This technique for determining a breadth score also moderates each of the underlying assumptions or premises behind the word count ratio and the commonness ratio. For example, if a patent claim is relatively shorter, but uses very uncommon terms, a patent practitioner might still consider the claim to be narrow due to the restrictive language in the claim. By defining a breadth score based on these two underlying assumptions, even shorter claims may be ranked not quite as broad if they use terms that are considered limiting or distinctive within a class in which an ontology is well developed)
- “calculating a subset weighted score for the subset of intellectual property assets that are associated with the” [corpus] “a respective subset breadth score for individual subset intellectual property assets that are associated with the” [corpus] “”; (Crouse ¶ [0019] 4th-5th sentences: comparative breadth score multiplied by 1st weight to determine weighted breadth score, the comparative portion count score multiplied by 2nd weight to determine weighted portion score, comparative differentiation score multiplied by 3rd weight to determine a weighted differentiation score. The comprehensive score for the document can then be determined based on average (mean, mode, lowest score, highest score, etc.) of the weighted breadth score, the weighted portion count score, and the weighted differentiation score.
Crouse ¶ [0029] last sentence: one or more of document portions may be give greater weight when determining the overall depth score. For example, independent claims may be given a greater weight than dependent claims when determining overall breadth score of a patent.
Crouse ¶ [0035] 4th-5th sentences: Of course the weight of independent claims may be something other than 4 times, such as 1.1×,1.2×,1.3×,2×,3×,5×, etc. In some instances, weighting independent claims greater than dependent claims for patents can provide better prediction for quality of the patents since patents that include more independent claims may include a broader claim scope than other patents or more reflect a different strategy of the claim drafter.
Crouse ¶ [0049] 5th-7th sentences: the number of unique words associated with each portion is then expressed as % of unique words within the corpus of words in the relevant documents. For example, if the corpus of words in the relevant documents includes 10000 unique words, and a given document portion (independent claim) includes 20 unique words within corpus of 10000 unique words, then % for the given document portion is 0.002%. If a second document portion (e.g. independent claim) also includes 20 unique words that are both within the corpus of 10000 unique words and exclusive of the words in the 1st (or any other previously processed) document portion, then the % for 2nd document portion is also 0.002%.
Crouse ¶ [0050] If the document of interest includes only those two portions, in some instances the overall differentiation calculation at 304 could made by summing reciprocal of each % for a differentiation calculation of 1000 (1/0.002+1/0.002), giving more weight to portions with a relatively small % of the unique words of the corpus. In other instances, the reciprocal of 1-% could be summed for each portion (1/(1−0.002)+1/(1−0.002)=2.004), giving more weight to portions with relatively large % of unique words of the corpus. In other instances, the reciprocal of the % for broadest portion could be used and the reciprocal of 1-% could be used for all other portions. In other instances, the summation could be made after weighting to the contribution of individual portions (in the context of patent documents, weighting the contribution of independent claims more heavily than the contribution of dependent claims). In this manner, a document with many document portions having unique words that are not common to other portion within the document will have a relatively high overall differentiation score and large footprint.
Crouse ¶ [0054] 5th-6th sentences: In some instances, comprehensive score calculation 402 may weight one or more of the 3 scores when determining the comprehensive scores for each of the documents. For example, if the comprehensive score calculation 402 gives twice as much weight to the comparative breadth scores 406 than each of the comparative portion scores 408 and the comparative differentiation scores 410, the comprehensive score for patent 349,983 would have value of ((87*2)+60+90) / 4 = 83.25).
Crouse ¶ [0094] 3rd-7th, 11th-12th sentences: In some instances, the relative weights of the word count ratio and the commonness score may be normalized. One technique for normalization is to set the highest respective values for both word count ratio and commonness score ratio to 100. If, for example, the highest word count ratio is h−wcr, then all of the wcr for the corpus will be multiplied by 100/h−wcr. Similar, normalization may be performed for the commonness score ratio using the highest commonness score ratio (h−csr). Of course, normalization values other than 100 may be used, such as 1000, 500, 50, 10, or the like. Both are numbers, but the relative effect on a breadth score may not directly correspond to the respective numerical values. For example, a word count ratio of 10 may have more or less impact on ultimate breadth than a commonness score ratio of 10. As such, the word count ratio may be weighted by first normalization value K (100/h−wcr) and commonness score ratio may be weighted by a second normalization value L (100/h−csr). When written in an equation: Breadth Score=K(wcr2)+L(csr2).
Crouse ¶ [0095] At 720, overall breadth scores for the documents are calculated. an overall breadth score may be calculated for each document being analyzed using the breadth scores for the document portions from the respective document. In some examples, calculating the overall breadth score for a document include taking an average of the breadth score(s) for document portions within the document. In some instances, calculating an overall breadth score for a document can include taking the average, median, mean or the like of the breadth score(s) of the document portions and producing a composite score. Additionally, in some instances, one or more of the breadth scores for one or more of the document portions for a document may be given more weight than one or more other breadth scores for one or more other document portions. For instance, if a document is a patent, breadth score(s) of independent claims(s) (e.g., the broadest independent claim) of the patent may be given more weight when determining the overall breadth score than breadth score(s) of dependent claim(s) within the patent.
- “calculating a [landscape] “breadth score for the subset of intellectual property assets that are associated with the [within the landscape] “by calculating an average of individual subset weighted scores”;
(Crouse ¶ [0097] 10th-14th sentences: in context where the documents are patents & the portions are claims, calculation 722 compare breadth score of broadest claim in a patent to breadth score of the broadest claims in all patents within the landscape, providing a rank ordering of the patent by broadest claim. Calculation 722 further compare average breadth of the claims in the patent to the average breadth of the claims in each of the patents within the landscape, providing a rank ordering of the patent by average claim breadth. Calculation 722 further compare the range of breadth of claims in the patent to the range of breadth of the claims in each of patents within the landscape, providing a rank ordering of the patent by range of claim breadth. Then calculation 722 may weight the rank order of each component score equally, to determine final breadth score. Then, calculation 722 may weight the rank order of each component score equally, to determine the final breadth score. Such an approach is based on an assumption that a relatively broad claim is more likely to encompass potentially infringing products, a relatively high average claim breadth reflects that likelihood across a range of independent and dependent claims, and a relatively high range of breadth reflects at least some claims are more likely to encompass limitations that reduce the viability of potential challenges to claim validity)
- “;
- “
- “displaying to a user a graphical user interface (GUI) configured to display at least one of the first comprehensive breadth score the second comprehensive breadth score, the competitive analysis report, or the [landscape] breadth score”
(Crouse Fig.7 step 724: generate a user interface that includes the comparative breadth scores, above. Also see Fig.7 ¶ [0098]: At 724, a UI is generated that includes the comparative breadth scores. For instance, a UI may be generated such that a comparative breadth score for one of the documents is displayed in proximity to the unique document identification number associated with that document. For example, the comparative breadth score for a patent may be displayed next to the patent number. In some instances, the UI may be textual UI or command-line interface that displays a line of text including at least the comparative breadth score and the unique document identification number. In some instances, the UI may include info on documents either to highlight a particular document (one having highest comparative breadth score out of all documents in the analyzed corpus), due to limitations of screen real estate such as on mobile devices, to minimize a volume of data transmitted across network etc.); “and”
-“wherein the GUI is dynamically updated based at least in part on receiving new user input”
(Crouse ¶ [0032] 5th, 8th sentences: When implement it as a graphical user interface, UI 120 may be generated by a cloud service that is accessible over a communications network such as the Internet. Any number of users may access UI 120 any time through specialized applications or through browsers (Internet Explorer, Firefox, Safari, Google Chrome, etc.) resident on their local computing devices. ¶ [0037] 1st sentence: The UI 206 may display, or otherwise present to a user, the comparative portion count scores, rankings based on the comparative portion count scores, and identifier for each of the analyzed documents. Any number of users may access the UI 206 any time through specialized applications or through browsers (Internet Explorer,Firefox, Safari, Google Chrome, etc.) resident on their local computing devices. Similarly, Crouse ¶ [0052] 1st, 8th sentences, Fig. 4 and ¶ [0055]-¶ [0057], ¶ [0158]-¶ [0159].
Crouse ¶ [0067] 8th-9th sentences: if a document does not match the specified document type, method 500 returns to 502 and a new document is received from the data repository. This portion of method 500 may proceed automatically and continually until all documents within the one or more data repositories have been analyzed. For example, see Fig.9 step 912: additional document portion -> Yes->feedback to 908 -> 920: generate the user interface. ¶ [0115] 1st sentence: At 912, it is determined whether there are any additional document portions in the document that are to be analyzed. If it is determined that there is an additional document portion to analyze (Yes), the method 900 repeats back at step 908 for the additional document portion. Similarly, Fig. 9 step 916: another document -> Yes-> feedback to 904 -> 920: generate the user interface. ¶ [0119] 1st sentence: At 916, it is determined whether there are any additional documents that that need to be analyzed. If it is determined that there is an additional document to analyze (i.e., Yes), the method 900 repeats back at step 904 for the additional document).
- “categorizing the individual intellectual property assets based at least in part on their respective weighted scores into distinct categories”;
(Crouse ¶ [0031] 2nd-3rd sentences: comparison of the breadth of a biotechnology patent to the breadth of a mechanical patent is less meaningful than comparing the breadth of one software patent to the breadth another software patent. Because the documents are given overall breadth scores with respect to the other documents in the same corpus, those overall breadth scores may be utilized to determine the comprehensive breadth scores for each of the documents. For example, at ¶ [0029] last 2 sentences: one or more of the document portions may be give a greater weight when determining the overall depth score. For example, independent claims may be given a greater weight than dependent claims when determining overall breadth score of a patent. ¶ [0035] 5th sentence: weighting independent claims greater than dependent claims for patents provide a better prediction for the quality of the patents since patents that include more independent claims may include a broader claim scope than other patents or more reflect a different strategy of the claim drafter. Also ¶ [0030] 6th sentence: Where the overall breadth score is based on the score of multiple document portions (e.g.…a weighted… composite of broadest, average…), calculation 118 compares that score or scores to the score or scores of the corresponding multiple document portions of other documents within the analysis. Similarly,
¶ [0054] 5th sentence: the comprehensive score calculation 402 may weight one or more of the 3 scores when determining the comprehensive scores for each of the documents. Similarly,
¶ [0103] 4th-5th sentences: document portions may be given more weight when calculating overall portion count scores for the documents. For instance, if the documents include patents, more weight may be given to the independent claims than to the dependent claims when calculating the overall portion count scores. Similarly, ¶ [0123], ¶ [0130]) “and”
- “
* While *
Crouse recites at ¶ [0031] the design for analysis captures the idea of comparing apples to apples when calculating comprehensive breadth scores. For instance, comparison of breadth of a biotechnology patent to the breadth of a mechanical patent is less meaningful than comparing breadth of one software patent to another software patent. Because the documents are given overall breadth scores with respect to the other documents in same corpus, those overall breadth scores may be utilized to determine the comprehensive breadth scores for each of the documents.
* And while *
Crouse ¶ [0097] 10th-14th sentences: refers to the landscape.
* Nevertheless beyond, a determination *
Crouse does not recite “technology breadth score” & “technology area” to clearly anticipate
- “calculating a subset weighted score for the subset of intellectual property assets that are associated with the technology area”; (independent Claim 1) [emphasis on untaught terms]
- “calculating a technology breadth score for the subset” (independent Claim 1) [emphasis added] as explicitly claimed.
* Also *
Crouse does not explicitly recite:
- “a first period of time” as required by: “calculating a first breadth score associated with a first period of time for the plurality of intellectual property assets…”;
- “second period of time” as required by: “determining at least a second comprehensive breadth score for the plurality of intellectual property assets associated with a second period of time, the second period of time being after the first period of time and after the plurality of intellectual property assets have issued”;
- “generating a competitive analysis report for the entity with the second comprehensive breadth score”
* However, in analogous IP analysis *
Edmund teaches or suggests “technology breadth score” & “technology area”
- “calculating a subset weighted score for the subset of intellectual property assets that are associated with the technology area”; (Edmund Fig.7, ¶ [0134] At 702, a data file is obtained from data repositories 102 in Fig.1. ¶ [0031] 2nd sentence: For instance, a collection of patents and/or applications may be gathered from data repository limited to a technology area. ¶ [0024] 5th-7th sentences: Comparison of the breadth of a biotechnology patent claim to the breadth of a mechanical patent claim is less meaningful than comparing the breadth of one software claim to another software claim. Comparison across different technology spaces cause the commonness of a given word to have vastly different impacts on overall claim breadth scores. For example, encryption might be found regularly in information technology patent claims and would only have a small negative impact on claim breadth, but that same word in a biotechnology claim may be relatively uncommon and represent a more significant limitation to claim breadth. Because the documents, or document portions, are given breadth scores with respect to the other documents in the same corpus those breadth scores may be ordered to produce a ranking with, e.g., 100 being the broadest (or alternatively the narrowest). ¶ [0126] 3rd-5h sentences: The relative weights of the word count ratio and the commonness score may be normalized. One technique for normalization is to set the highest respective values for both word count ratio and commonness score ratio to 100. If, for example, the highest word count ratio is h-wcr, then all of wcr for the corpus will be multiplied by 100/h-wcr)
- “calculating a technology breadth score for the subset”
(Edmund ¶ [0031] 2nd sentence, ¶ [0024] 5th-7th sentences, ¶ [0126] 6th-12th sentences: Similar normalization may be performed for the commonness score ratio using the highest commonness score ratio (h-csr). Of course normalization values other than 100 may be used such as 1000, 500, 50, 10, etc. Both are numbers but the relative effect on an overall score (e.g., claim breadth) may not directly correspond to the respective numerical values. For example, a word count ratio of 10 may have more or less impact on ultimate breadth than a commonness score ratio of 10. However, without normalization both contribute equally to the overall score. Thus, the word count ratio may be weighted by a first normalization value K (e.g. 100/h-wcr) and the commonness score ratio may be weighted by a second normalization value L (e.g., 100/h-csr). When written in an equation: Overall Score=√{square root over (K(wcr 2)+L(csr 2))}. ¶ [0145] At 720, a claim breadth score is generated from the word count score and the commonness score. The claim breadth score may be calculated by square root of the sum of both the square of the word count score and the square of the commonness score. The relative impact of the word count score and of the commonness score may be modified by weighting the raw score values to create weighted scores. This may be repeated for each patent claim under analysis so that each patent claim is now associated with a new piece of data representing an associated claim breadth score. In an implementation, the claim breadth score may be generated by equation 2 above)
- “a first period of time” as required by “calculating a first comprehensive breadth score associated with a first period of time for the plurality of intellectual property assets…”;
- “second period of time” with respect to “determining at least a second comprehensive breadth score for the plurality of intellectual property assets associated with a second period of time, the second period of time being after the first period of time and [when] “the plurality of intellectual property assets have issued”;
(Edmund Fig.7, ¶ [0149] 1st-3rd sentences: one or more of the steps of method 700 described from 702-724 may be performed for different time in prosecution of a patent or a corpus of patent documents. For example, a claim breadth score may be determined for patents in a corpus at a 1st point in time, such as when the patents were filed, or before amendments were made to the claims (or any other point in prosecution). Additionally, the claim breadth scores may be determined for the patents at a 2nd point in time such as a point in time corresponding to when claims were allowed (or any other time in prosecution)
- “generating a competitive analysis report for the entity with the second comprehensive breadth score” (Edmond ¶ [0024] 1st-2nd,6th-8th sentences ranking 116 ranks the analyzed documents. Breadth calculation 114 is performed within the context of the other documents in a corpus. Comparison across different technology spaces cause the commonness of a given word to have vastly different impacts on overall claim breadth scores. For example, encryption might be found regularly in information technology patent claims and would only have a small negative impact on claim breadth, but that same word in a biotechnology claim may be relatively uncommon and represent a more significant limitation to claim breadth. Because the documents, or document portions, are given breadth scores with respect to the other documents in the same corpus those breadth scores may be ordered to produce a ranking [or report] with, e.g., 100 being the broadest (or alternatively the narrowest). ¶ [0025] 1st sentence: The user interface (UI) 118 may display, or otherwise present [or report] to a user, the breadth scores, the ranking, and an identifier for each of the analyzed documents. ¶ [0028] 1st sentence: the difference [or comparison] in breadth score can be used to generate the ranking 206 and is an indication of the amount of variation between the breadth 204 of various patent claims or other document portions. For example, see
Edmund ¶ [0148] 1st-3rd sentences: In 724, a UI is generated displaying ranking [or report] generated at 722. The ranking shows the claim breadth score of the patent claim under analysis. The UI may also display other claim breadth scores of other patent claims from the same corpus. Additionally, the UI may display patent numbers associated with the individual patent claims.
Edmund ¶ [0149] 4th,6th sentences: a difference [or comparison] in claim breadth scores between the 1st and 2nd point may be determined, and average change [or comparison] in patent claim breadth scores may be determined for individual patents, or for a corpus of patents. an average change [or comparison] in patent claim breadth scores may further be used to determine claim breadth scores for other patents. For example, an average change in patent breadth scores for invention patents assigned to a particular assignee or filed by a particular application may be applied to utility model patents for same assignee or applicant
Edmund ¶ [0165] 9th-10th sentences: the documents may be ranked based on these breadth scores derived from their broadest document portion. Alternatively, the breadth scores assigned to document may be based on the breadth of a lowest ranked document portion, an average of rankings of the multiple document portions, median of rankings the multiple document portions, or other metric derived from the individual breadth scores of portions of a document.
¶ [0166] In an implementation, ranking module 832 may additionally bin the results of the ranking into one of a set number of values. One binning implementation is by percentiles. Thus top 1% of the analyzed documents in terms of breadth would be all given a rank of 100 even if the individual documents had slightly different breadth scores. The binning divide the ranked documents into any number of different bins such as three different bins (e.g., high, medium, and low), 10 different bins, 100 different bins, or more. Thus, instead of 100000 documents ranked from 1 to 100000 in terms of breadth with each ranking being unique, each document may have a rank from 1 to 100 with several documents sharing each numerical level. Other examples at ¶ [0177]-¶ [0180] noting various reports including Claim Breadth), Innography PatentStrength, and Relecura Star Rating).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Crouse “system” to have further included Edmund’s teachings or suggestions in order to have mitigated the cost and relatively slow speed of manual, human analysis makes to make it effectively possible and practicable to have performed document analysis at the scale, speed, and cost desired in many industries (Edmund ¶ [0001] 3rd sentence in view of MPEP 2143 G), and have improved upon other automated techniques to provide document processing by achieving a result that quantitatively improves upon manual, human processing (Edmund Abstract last sentence, ¶ [0173] 2nd sentence in view of MPEP 2143 G). The predictability of such modification would have been further corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Crouse ¶ [0163] in view of Edmund at ¶ [0183]. Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor, dealing with analysis of intellectual property. In such combination each element merely would have performed same analytical and displaying function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Crouse in view of Edmund, the to be combined elements would have fitted together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
* While *
Crouse in combination with Edmund teaches or suggests:
- “determining at least a second comprehensive breadth score for the plurality of intellectual property assets associated with a second period of time, the second period of time being after the first period of time and [when] “the plurality of intellectual property assets have issued”;
* Still *
Crouse in combination with Edmund does not explicitly recite preposition “after” as in:
- “…comprehensive breadth score for the plurality of intellectual property assets associated with a second period of time, the second period of time being after the first period of time and after the plurality of intellectual property assets have issued” as explicitly claimed [bolded emphasis added].
Crouse/Edmund also does not explicitly recite their data as “vector representations” as per
- “generating vector representations of a plurality of intellectual property assets associated with an entity, individual intellectual property assets including respective portions of text, wherein the vector representations represent a lower dimensional version of the plurality of intellectual property assets that, when stored, requires less storage than storing the plurality of intellectual property assets”,
- “accessing a database storing the vector representations”;
- “calculating, for the individual intellectual property assets of the plurality of intellectual property assets and utilizing the vector representations, a first breadth score”… as claimed.
* However *
Perkowski in analogous art of determining the meets and bounds (i.e. breadth) of claims at infringement, and hence after allowance of claims, teaches or suggests:
- “…comprehensive breadth score for the plurality of intellectual property assets associated with a second period of time, the second period of time being after the first period of time and after the plurality of intellectual property assets have issued”;
(Perkowski ¶ [0025] claim interpretation (i.e claim construction) is the centerpiece of patent litigation. In a patent infringement lawsuit, at some point, the patent claims may be interpreted to determine how infringement, validity, and other issues under the patent will be measured. Patents typically have multiple claims, and each claim is considered separately for issues of infringement and validity. Specifically, per Perkowski ¶ [0358]… the user can analyze the patent claim prosecution history of any granted patent including any allowed patent claims…: the Patent Claim Scope Interpretation (i.e. Construction) Analysis Mode of operation (Mode 1) specified in Figs.19 through 27F. For example, at
Perkowski ¶ [0178]: Fig.25A [below] is a Claim Scope Concept Based Markup model for any patent claim expressed in natural language, and directed to a useful invention, setting forth the meets and boundaries (i.e. the scopes and limits) of patent protection to be afforded to the invention covered by the patent claim which is composed of a plurality of claim limitation language strings (CLLS), wherein during the markup process, (i) each claim limitation language string (CLLS) is assigned a claim limitation identifier or index (CLID), (ii) Claim Scope Concept Phrase (CSCP) composed of set of words (W1…WN) describing the Scope Concept encompassed by the Claim Limitation Language String (CLLS), is assigned to the CLLS and (iii) a Claim Scope Concept Structure Identifier (CSCSID) is assigned to the CLLS to identify the entire CSC data structure, in accordance with the principles of the present invention. Similarly see Fig.93
Perkowski ¶ [1239] (xx) notes on the user's understanding on patent claim construction, scope/boundary conditions, and as well as evidentiary proof issues and concerns, such as identification of evidence by plaintiff and defendant embodying the use of one or more scope concepts associated with particular sub-limitations in the allowed claims on the granted patent involved in litigation. (Step S2). [such litigation interpreted after the plurality of intellectual property assets have issued]).
* Further *
Perkowski, also teaches or suggests “vector representations” as required by:
- “generating vector representations of a plurality of intellectual property assets associated with an entity, individual intellectual property assets including respective portions of text, (Perkowski ¶ [0055] the language of patent limitations is also mapped to specific corresponding portions of statements made by the examiner and/or applicants/owners during the course of the patent prosecution history of the patent claims. ¶ [0124] v) generate claim scope concept (CSC) based prior art search vectors based on the scope concept analyzed patent claims; (vi) use the CSC-based prior art search vectors to search for new and non-cited prior art references relating to the subject matter of the patent claims in the published patent application;
Perkowski ¶ [0272] Fig.97 is a schematic representation illustrating the generation of a CSC-based Search Vector (CSC-SV) from the data and meta-data contained in the Claim Scope Concept Structure (CSCS) of each Claim Limitation Language String (CLLS) of a Claim being marked-up using the CSCSML. ¶ [0273] Fig.98 is a schematic representation illustrating use of the set of generated CSC-based Search Vectors to search and find a set of Prior Art References {PAR} containing prior art disclosure (e.g. searchable text and searchable graphics and/or images) discovered by the CSC-Based Search Vectors, and generating a set of Prior Art Search Records {PASR} for the retrieved Prior Art References (PAR); ¶ [0274] Fig.99 is a schematic representation illustrating the analyzing Prior Art Reference Record (PARR) generated for each retrieved Prior Art Reference (PAR) discovered by the CSC-based Search Vectors, using the Claim Scope Concept (CSC) based Prior Art Reference Analysis Schema (or GUI), and the mapping of relevant prior art disclosure (discovered by the CSC-based Search Vectors) into corresponding CSC-indexed data fields in the Claim Scope Concept (CSC) based Prior Art Reference Analysis Schema (or GUI); ¶ [0288] Fig.113 is schematic representation of (schema for) a Claim Scope Concept (CSC) Based Search Vector and Prior Art Reference Profile document or record designed for a set of patent claims, organized by Claim Scope Concept (CSC) Structure Number, and generated while prior art references are being scope concept analyzed using the Claim Scope Concept Based Prior Art Reference Schema of Fig.10; ¶ [0289] Fig.114 is a schematic representation of (i.e. schema for) the Claim Scope Concept (CSC) Based Search Vector and Prior Art Reference Profile document or record designed for a set of patent claims, organized by Claim Number, and generated while prior art references are being scope concept analyzed using the Claim Scope Concept Based Prior Art Reference Schema of Fig.10)
”,
- “accessing a database storing the vector representations”;
(Perkowski ¶ [0106]: …search vectors for use in searching for and discovering prior art disclosure information located anywhere along …patent and technical databases,
Perkowski ¶ [0273] Fig.98 illustrate use of set of generated CSC-based Search Vectors to search for and find set of Prior Art References {PAR} containing prior art disclosure (searchable text and searchable graphics and/or images) discovered by the CSC-Based Search Vectors, and generating a set of Prior Art Search Records {PASR} for the retrieved Prior Art References (PAR);
Perkowski ¶ [0274] Fig.99 is a schematic representation illustrating the analyzing the Prior Art Reference Record (PARR) generated for each retrieved Prior Art Reference (PAR) discovered by the CSC-based Search Vectors, using the Claim Scope Concept (CSC) based Prior Art Reference Analysis Schema (or GUI), and the mapping of relevant prior art disclosure (discovered by the CSC-based Search Vectors) into corresponding CSC-indexed data fields in the Claim Scope Concept (CSC) based Prior Art Reference Analysis Schema (or GUI);
Perkowski ¶ [0786] in Step P in Fig.41C, GUI screens in Figs.43BB,37BB are employed, so the user easily synthesize search vectors, based on patent claim prosecution history language linked to claim limitations, and then store these search vectors in the system database. Specifically, while logged into the patent analysis and charting system, use the GUIs and methods of the system to select scope concept definitions and patent claim prosecution history language linked to claim limitations and stored in the database system, to synthesize search vectors for use against prior art search engines. Automated methods for scope concept based search vector based searching, and prior art reference retrieval, tagging and mapping to corresponding scope concept query fields in scope concept based prior art reference analysis GUIs will be described in great technical detail below, after description of the workflow process of Mode 4.
Perkowski ¶ [1305] in STEP G in Fig.81B, the Applicant/Owner uses the Scope Concept Prior Art Search Vectors to conduct prior art searches against various patent databases (US, PCT, EPO, JPO, and other foreign searches), technical information databases, and the World Wide Web using Google Search Engines; such search efforts can be initiated through the Search Module of the PCW, or offline, as the case may be).
Perkowski ¶ [1431] in STEP E of Fig.95B, the process involves using the set of generated CSCS-based Search Vectors {<CSCSID-SV>} to (i) search for and find a set of Prior Art References {PAR}. During this step of the search process, each Prior Art References that matches at least one of the CSCS-based Search Vectors is retrieved and stored for analysis. Also, a Prior Art Search Record (PASR) is generated for each retrieved Prior Art Reference, wherein each Prior Art Search Record (PASR) is created by tagging and/or linking the CSCS-based Search Vector used to retrieve a particular Prior Art Reference, with (i) the Prior Art Reference (PAR), (ii) the cited Prior Art Disclosure contained in the Prior Art Reference, and (iii) the Claim Scope Concept Structure Identifier (CSCSID) so as to create the Prior Art Search Record (PASR) that is stored in the system database for the corresponding CSCS-based Search Vector. This step of the process illustrated in FIG. 98, wherein a set of generated CSC-based Search Vectors are used to search for and find a set of Prior Art References {PAR} containing prior art disclosure (e.g. searchable text and searchable graphics and/or images) retrieved/discovered by the CSC-Based Search Vectors, and thereafter, a set of Prior Art Search Records {PASR} are generated for the retrieved Prior Art References (PAR)
Perkowski ¶ [1432] indicated in STEP F of Fig.95B, the process involves using the Prior Art Search Record (PASR) generated for each retrieved Prior Art Reference and the Claim Scope Concept Based Prior Art Reference Analysis Schema, to generate a set of Prior Art Reference Claim Scope Concept (CSC) Profile documents for the set of retrieved Prior Art References. During this step of the process, each generated Prior Art Reference Claim Scope Concept Structure Profile document comprises, for each Claim in the set of Claims, (i) a list of Claim Scope Concept Structures (CSCS), and (ii) an indication of whether or not each CSCS has been substantiated by Prior Art Disclosure contained in at least one Prior Art Reference that has been retrieved during the prior art search using the CSCS-based Search Vector linked to the CSCS. This step of the process is illustrated in Fig.99, wherein the Claim Scope Concept (CSC) based Prior Art Reference Analysis Schema (or GUI) is used to analyze the prior art reference record generated for each retrieved prior art reference discovered by the CSC-based search vectors.
Perkowski mid-¶ [1734] (d) generating a set of CSCS-based search vectors based on the data and meta-data contained in the Claim Scope Concept Structure (CSCS) of each Claim Limitation Language String contained in each Claim; (e) using the set of CSCS-based Search Vectors to search for and find a set of Prior Art References, each matching at least one of the CSCS-based Search Vectors, and a generate set of Prior Art Search Records for the retrieved Prior Art References, wherein each prior art search record is created by linking to or tagging the CSCS-based Search Vector used to retrieve a particular prior art reference, with the Prior Art Reference, cited Prior Art Disclosure and the Claim Scope Concept Structure Identifier (CSCSID), to create the Prior Art Search Record that is stored in the system database for the corresponding CSCS-based Search Vector; (f) using the Prior Art Search Record (PASR) generated for each retrieved prior art reference, and the Claim Scope Concept Based Prior Art Reference Analysis Schema, to generate a set of Prior Art Reference Claim Scope Concept Profile Documents for the set of retrieved Prior Art References, wherein each Prior Art Reference Claim Scope Concept Structure Profile document comprises, for each Claim in the set of Claims, a list of Claim Scope Concept Structures (CSCS), and indication of whether or not each CSCS has been substantiated by prior art disclosure from at least one prior art reference retrieved from the search, and indexed with the CSCS-based search vector that was used to retrieve the prior art reference containing the prior art disclosure; (g) optionally, organizing the Claim Scope Concept Structures (CSCS) in the Prior Art Reference Claim Scope Concept Structure Profile documents by grouping the Claim Scope Concept Structures (CSCS) having the same or similar Inventive Feature Phrases (IFP), or at least a predetermined number of common IFP words, so that a human reviewer or analyzer can see all Claim Scope Concept Structures (CSCSs); (h) automatically processing the set of Prior Art Reference Claim Scope Concept Profile documents to analyze the retrieved set of Prior Art References, and generate Claim Patentability Analyses based on the analyzed retrieved Prior Art References, and then generate Claim Patentability Analysis Charts according to the present invention; (i) ranking the Prior Art References retrieved during the search results according to which Prior Art References have the Maximum Number Of Claim Scope Concepts (CSC) Substantiated By Prior Art Reference Disclosure (Mapped By The CSC-Based Search Vectors), Then Order Prior Art Reference Analysis by a human subject matter expert according to The Highest Priority Of CSC-Ranking)
- “calculating, for the individual intellectual property assets of the plurality of intellectual property assets and utilizing the vector representations, a first breadth score based at least in part on a first word count score”
(Perkowski ¶ [0298] Fig.118A-C, taken together, show exemplary Excel-based cumulative prior art reference chart generated by system of Fig. 94, during or independent from the process of the present invention, in connection with patent claim analysis, search vector generation, prior art searching, prior art reference retrieval and analysis, and/or claim patentability analysis and charting operations. ¶ [0812] 2nd sentence: generation of PHH-based search vectors will include claim scope concepts (CSCs) and others terms, phrases and words abstracted during prosecution history analysis. As previously stated by ¶ [0178]: (ii) a Claim Scope Concept Phrase (CSCP) composed of a set of words (W1…WN) describing the Scope Concept encompassed by the Claim Limitation Language String (CLLS), is assigned to the CLLS. Similar, ¶ [0268], ¶ [0486], ¶ [01420], ¶ [1421] 1st sentence: in the CSCML model of Fig.93, the set of claim limitation language strings (CLLS) are strung together like beads on a string to form a complete closed ring structure, to suggest the encompassing of n-dimensional linguistic space by the words and phrases used to create the patent claim, where n is the number of words (lexical units) to create the patent claim.
Perkowski ¶ [1325] As indicated in STEP AA in Fig.81G, Examiner logs into the PAIR/EFS Portal of the system, reviews and examines the presented Claims and corresponding Claim Scope Concept Profiles in view of…, (ii) the set of Scope Concept Profiles for the set of presented Claims, (iii) the Scope Concept Based Prior Art Search Vectors generated and used to conduct a prior art search against the subject matter of the Claims, (iv) the Scope Concept Prior Art Reference Profiles for each of the analyzed prior art references, and (v) the Claim Patentability Analysis Charts automatically generated by the Claim Patentability Analysis Module using the Claims and the disclosed Scope Concept Prior Art Reference Profiles; wherein documents (ii) through (v) are deemed to constitute scope concept instruments and as they are useful in determining the scope and boundaries of the pending Claim under examination.
Perkowski ¶ [1423] 2nd-9th sentences: in Fig. 94, a parsing module (e.g. algorithm) is used to automatically parse the patent claims into claim limitation language strings (CLLS), typically using punctuation marks as guides to parsing boundaries. The parsed claim limitation language strings are assigned Claim Scope Concepts (CSC) as described hereinabove. This process is continued until a Set Of Claim Scope Concepts is formed for the Set Of Patent Claims under analysis. Then two paths of processing continue. Along the first path, the set of claim scope concepts for the patents claims under analysis are used to generate a Claim Scope Concept (CSC) Based Prior Art Reference Analysis Schema (and GUI), and then this Schema (and GUI) are provided to an automated prior art reference analyzer. Along the second path, a Claim Scope Concept (CSC) Based Search Vector Profile is generated for each patent claim under analysis, and then a Master Set of Claim Scope Concept (CSC) Based Search Vector Profiles are generated for the set of pending patent claims under analysis. A set of CSC-based Search Vectors are generated from the Master Set of Claim Scope Concept (CSC) Based Search Vector Profiles, and then supplied to the Search Engines which include patent and technical databases covering patents and technology around the world, in different countries and languages, with language translators where and as necessary, in a matter known in the language translation art. The Prior Art Search Engines deliver discovered prior art references containing prior art disclosure (e.g. searchable text and/or searchable graphics and/or images) specified by CSC-Based Search Vectors. Any given discovered prior art reference may contain prior art disclosure that is relevant to one or more or all of the claim limitation language strings (CLLS) of one or more Claims under analysis. Each discovered Prior Art Reference is then tagged with the CSC-based Search Vector that discovered the Prior Art Reference during the prior art search. The Retrieved prior art references tagged with the CSC-based search vectors are then provided to the automated prior art reference analyzer, operating accordance to the CSC-based Prior Art Reference Analysis Schema that was generated for the set of patent claims under analysis. The automated prior art reference analyzer processes (i) each CSC-based prior art reference (i.e. searchable document) tagged with the CSC-based search vectors that discovered the prior art reference during search, and (ii) the CSC-based Prior Art Reference Analysis Schema, described in Fig.110, so as to generate a prior art reference CSC-based profile document shown in Fig.121, for each prior art reference retrieved during the search).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified the combination of Crouse / Edmund to have included Perkowski’s teachings to have further improved predictability of claim construction/interpretation (Perkowski ¶ [0021] 2nd sentence, ¶ [0108] - ¶ [0111], ¶ [0440] 2nd sentence in view of MPEP 2143 G). The predictability of such modification would have been further corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Crouse ¶ [0163] in view of Edmund at ¶ [0183] and in further view of Perkowski ¶¶ [0009] 3rd sentence, [0132], [1539], [1638], [1754].
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor of intellectual property analysis. In such combination each element would have merely performed same analytical and displaying function as separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Crouse in view of Edmund and in further view of Perkowski, the to be combined elements would have fitted together like puzzle pieces in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the combination results would have been predictable (MPEP 2143 A).
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Perkowski Fig.25A in support of rejection arguments
Crouse in combination with Edmund and Perkowski does not explicitly recite:
“wherein the vector representations represent a lower dimensional version of the plurality of intellectual property assets that, when stored, requires less storage than storing the plurality of intellectual property assets”,
- “presenting, via the GUI, a visualization of the distinct categories” as claimed. However,
Zhang, in analogous art of vector representation in a data set teaches or at least suggests
- “wherein the vector representations represent a lower dimensional version of the plurality of intellectual property assets that, when stored, requires less storage than storing the plurality of intellectual property assets”, (Zhang mid-¶ [0035] Word embedding vectors reduce [or lowers] the number of dimensions thus increasing the training speed of the model and reducing system memory requirements…. program 150 utilizes dimension reducing techniques, such as feature extraction, low-dimensional embedding, and kernelling, to reduce the number of dimensions required to represent the training data and features. Reducing the numbers of required dimensions (e.g., features, variables, etc.) reduces the needed time and storage space….)
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Crouse in combination with Edmund and Perkowski to have further included clarification provided by Zhang that “the vector representations represent a lower dimensional version of the plurality of intellectual property assets that, when stored, requires less storage than storing the plurality of intellectual property assets”, to have reduced both the needed time and storage space, and further go beyond such benefits by having improved interpretation of the parameters of the cognitive model, to have further allowed for data visualization in low dimensions while having avoided peaking phenomena (Zhang ¶ [0035[ in view of MPEP 2143 G). The predictability of such modification would have been corroborated by the broad level of skills of one of ordinary skills in the art as articulated by Crouse ¶ [0163] in view of Edmund at ¶ [0183] and in further view of Perkowski ¶ [0009] 3rd sentence, ¶ [0132], ¶ [1539], ¶ [1638], ¶ [1754] and in further view of Zhang ¶ [0014], ¶ [0068]. Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with manipulation and representation of data. In such combination each element would have merely performed the same analytical, storage, processing and display function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Crouse / Edmund / Perkowski in further view of Zhang, the to be combined elements would have fitted together, like pieces of a puzzle in a logical commentary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
* Further still *
Chan however in analogous IP analysis teaches or suggests:
- “presenting, via the GUI, a visualization of the distinct categories”
(Chan ¶ [0477] data mining/analysis system 102 can provide discovery functions and research analyses that enable users to discover and invest in under-appreciated or underutilized patents, or to conduct due diligence on patents for deals where IP is the driving catalyst. The data mining/analysis system 102 can integrate quantitative and qualitative methods to help our clients visualize under-appreciated or underutilized patents quickly and efficiently. For example, at
Chan ¶ [0478] Fig.22 [below] is a screenshot of discovery functions and research analyses in the form of topology analytics. data mining/analysis system 102 provide topology analytics that offer data visualization and interactive graphics that allow US patents that have potential impact in their respective technology sectors to be visually identified quickly and efficiently.
Chan ¶ [0480] where an investment value for a particular type of patent is particularly known (e.g. based on incoming documents identifying known damages recovered from litigation or from business transactions identifying the purchase values of patents in a similar class or category), this investment value can be fed to the data mining/analysis system 102 to identify patents that are more valuable for investment purposes [as example of investment decisions]. This subset of patents can also be linked to other subsets of patents to generate a comprehensive mapping that identifies the stronger, weaker, more valuable, and/or less valuable patents in a particular set of patents or patents related to a particular industry or sector.
Chan ¶ [0481] whether users are interested in offensive-based portfolio to protect a business' core technology and drive profits, or defensive-focus strategy to capture unclaimed territory surrounding core technology that blocks alternative designs and ensures freedom to operate, the topology analytics in Fig.22 allow users to become acquainted with the current patent landscape, understand economic impact of granted patents on old and new, small to large businesses and identify rapid technological trends that closely align with their investment interests
Chan ¶ [0482] with topology analytics users quickly identify which technology is and remains most active, how patent filing patterns have changed over time, and where important technology battles and business stakes are taking place. Also, with topology analytics, a visual representation of information can be provided in a way that is easy to understand, allowing for stakeholders and contributors to quickly capture information they need for their own due diligence research.
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Chan Fig.22 in support of rejection arguments
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified modify Crouse/ Edmund / Perkowski / Zhang, “system” to have included Chan’s teachings above, in order to have had more effectively processed large amounts of data (Chan ¶ [0004] 1st sentence, ¶ [0088] & MPEP 2143 G). For example, the data mining / analysis provided by Chan would have improved accuracy and robustness of trained models (Chan ¶ [0201] 2nd sentence, ¶ [0206], ¶ [0211], ¶ [0224] last sentence, ¶ [0231] last sentence, MPEP 2143 G) and would have better integrated quantitative and qualitative methods to have helped clients visualize under-appreciated or underutilized patents quickly and efficiently (Chan ¶ [0477] last sentence & MPEP 2143 G and/or F). Moreover, the data mining/analysis provided by Chan would have further provided topology analytics that would have offered better data visualization and interactive graphics that would have allowed the patents that have potential impact in their respective technology sectors to be visually identified quickly and more efficiently. Moreover, Chan would have further identified the most effective path to resolution and drive proactive settlement terms (Chan ¶ [0044] 3rd sentence & MPEP 2143 G and/or F) as incentivized by market foresees necessitating safeguarding original ideas determining the survival of a company to protect and defend a patent holder's intellectual property (IP) from patent infringers and from costly trials (Chan ¶ [0044] 2nd-4th sentences & MPEP 2143 G and/or F).
The predictability of such modification would have been further corroborated by the broad level of skill of one of ordinary skills in the art as further articulated by Crouse ¶ [0163] in view of Edmund at ¶ [0183,] in view of Perkowski ¶ [0009] 3rd sentence, ¶ [0132], ¶ [1539], ¶ [1638], ¶ [1754] in view of Zhang ¶ [0014], ¶ [0068] and in further view of Chan at ¶ [0511]-¶ [0512].
Further still, the claimed invention could have also been viewed as a mere combination of old elements in a similar analyzing legal documents field of endeavor. In such combination each element merely would have performed same analytical, statistical and organizational function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements evidenced by Crouse / Edmund / Perkowski / Zhang in further view of Chan, the to be combined elements would have fitted together like a puzzle pieces, in logical, complementary, technological feasible and economically desirable manner. Thus, it would have been reasoned that the results of the combination were predictable (MPEP 2143 A).
Claim 2 Crouse /Edmund/Perkowski/ Zhang/Chan teaches all limitations in claim 1. Further
Crouse still recites at Fig.4 ¶ [0056] 4th sentence: One of interactive elements 418 may be activated in response to a command generated on input device to select one of the documents
Crouse still recites at ¶ [0057] in some instances, the UI 412 can include group scores 420 for the documents under analysis. For instance, a document may be related to one or more other documents that are being analyzed. For example, a patent may be included in a patent family, which can include two or more patents. Thus, the group scores 420 can include a score for each of the documents that is included in a group. In some instances, the group score 420 for a document can include the average of each of the comprehensive scores of the documents within the group. In some instances, the group score 420 for a document can include the median, mode, lowest comprehensive score, highest comprehensive score, or the like of the comprehensive scores of the documents within the group. In some instances, one or more of the documents under analysis may not be included in a group and as such, may not include a group score 420. For instance, the first two patents included in the UI 412 include respective group scores 420, while the last two patents do not include respective group scores 420.
* However *
Crouse does not explicitly teach: “wherein the technology breadth score comprises a first technology breadth score associated with a first period of time and the operations further comprising:
- “determining at least a second technology breadth score for the subset of intellectual property assets associated with the second period of time”; “and”
- “presenting the first technology breadth score and the second technology breadth score to the user via the GUI”
* Nevertheless *
Edmund in analogous analysis of intellectual property teaches/suggests: “wherein the technology breadth score comprises a first technology breadth score associated with a first period of time and the operations further comprising:
- “determining at least a second technology breadth score for the subset of intellectual property assets associated with the second period of time”; “and”
(Edmund Fig.7, ¶ [0134] At 702, a data file is obtained. In an implementation, the data file may be obtained from one of the data repositories 102 show in Fig.1. ¶ [0031] 2nd sentence: For instance, a collection of patents and/or applications may be gathered from a data repository limited to a technology area. Next at Fig.7, ¶ [0149] 1st-3rd sentences: In some examples, one or more of the steps of method 700 described from 702-724 may be performed for different time in prosecution of a patent or a corpus of patent documents. For example, a claim breadth score may be determined for patents in corpus at 1st point in time such as when the patents were filed, or before amendments were made to the claims (or any other point in prosecution). Additionally, the claim breadth scores may be determined for the patents at 2nd point in time, such as point in time corresponding to when the claims were allowed or any time in prosecution); “and”
- “presenting the first technology breadth score and the second technology breadth score to the user via the GUI” (Edmund ¶ [0025] 1st sentence: user interface (UI) 118 display, or otherwise present to a user, the breadth scores, the ranking, and an identifier for each of the analyzed documents. For example at Fig.7 and ¶ [0149] 1st-3rd sentences: the steps of method 700 described from 702-724 may be performed for different time in prosecution of a patent or a corpus of patent documents. For example, a claim breadth score may be determined for patents in a corpus at a first point in time, such as when the patents were filed, or before amendments were made to the claims (or any other point in prosecution). Additionally, the claim breadth scores may be determined for the patents at a second point in time, such as a point in time corresponding to when the claims were allowed (or any other time in prosecution). Next at ¶ [0178], Fig.7 step 724: generate UI displaying the ranking).
Rationales to have modified/combined Crouse / Edmund are above and reincorporated.
Rationales to have modified/combined Crouse /Edmund/Perkowski/ Zhang/Chan were also presented above.
Claim 3 Crouse /Edmund/Perkowski/Zhang/Chan teaches all limitations in claim 1. Further
Crouse teaches/suggests: “the operations further comprising receiving a new user input via the GUI” (Crouse ¶ [0055] 6th-7th sentences: GUI is a type of user interface that allows users to interact with electronic devices through graphical icons and visual indicators such as secondary notation, instead of text-based user interfaces, typed command labels or text navigation. Actions in GUI performed through direct manipulation of the graphical elements using a pointing device such as a mouse, stylus, or finger. Fig.9,912,916 and ¶ [0056] 1st sentence: There is entry for documents in UI 412 and info about those documents) “and causing the GUI to present metric data associated with the first comprehensive breadth score in response to the new user input” (Crouse ¶ [0131] At 1008, a UI is generated that includes one or more of the comprehensive scores. For instance, a UI may be generated such that a comprehensive score for one of the documents is displayed in proximity to the unique document identification number associated with that document. For example, the comprehensive score for a patent may be displayed next to the patent number. In some instances, the UI may be textual UI or command-line interface that displays a line of text including at least the comprehensive score and the unique document identification number. In some instances, the UI may include information on documents either to highlight a particular document (e.g., one having a highest comprehensive score out of all the documents in the analyzed corpus), due to limitations of screen real estate such as on mobile devices, to minimize a volume of data transmitted across a network, or for other reasons. ¶ [0054] 4th sentence: as shown for patent 349,983, comprehensive score 404 includes an average of comparative breadth score 406 (from processing pipeline 100), comparative portion count score 408 (from processing pipeline 200), and comparative differentiation score 410 (from processing pipeline 300) for the patent. ¶ [0067] 8th-9th sentences: if a document does not match the specified document type, method 500 returns to 502 and a new document is received from the data repository. This portion of method 500 may proceed automatically and continually until all documents within the one or more data repositories have been analyzed. For example, see Fig.9 step 912: additional document portion->Yes->feedback to 908 -> 920: generate the user interface. ¶ [0115] 1st sentence: At 912, it is determined whether there are any additional document portions in the document that are to be analyzed. If it is determined that there is an additional document portion to analyze (Yes), the method 900 repeats back at step 908 for the additional document portion. Similarly, Fig. 9 step 916: another document -> Yes-> feedback to 904 -> 920: generate the user interface. ¶ [0119] 1st sentence: At 916, it is determined whether there are any additional documents that that need to be analyzed. If it is determined that there is an additional document to analyze (i.e., Yes), the method 900 repeats back at step 904 for the additional document).
Claim 5 Crouse /Edmund/Perkowski/Zhang/Chan teaches all the limitations in claim 1 above.
Crouse does not teach: “wherein the second comprehensive breadth score is different from the first comprehensive breadth score based at least in part on: a first intellectual property asset that is filed; a second intellectual property asset that is granted; a third intellectual property asset that is expired; a fourth intellectual property asset that is abandoned; or a breadth score for a fifth intellectual property asset that is changed” as claimed.
Edmund in analogous analysis of intellectual property teaches or suggests: “wherein the second comprehensive breadth score is different from the first comprehensive breadth score based at least in part on: a first intellectual property asset that is filed; a second intellectual property asset that is granted; a third intellectual property asset that is expired; a fourth intellectual property asset that is abandoned; or a breadth score for a fifth intellectual property asset that is changed” (Edmund Fig.7 and ¶ [0149] 1st-3rd sentences: In some examples, one or more of the steps of method 700 described from 702-724 may be performed for different time in prosecution of a patent or a corpus of patent documents. For example, a claim breadth score may be determined for patents in a corpus at a first point in time, such as when the patents were filed, or before amendments were made to the claims (or any other point in prosecution). Additionally, the claim breadth scores may be determined for the patents at a second point in time, such as a point in time corresponding to when the claims were allowed (or any other time in prosecution)
Rationales to have modified/combined Crouse/Edmund are above and reincorporated.
Rationales to have modified/combined Crouse /Edmund/Perkowski/Zhang/Chan were also presented above, with Perkowski also teaching or suggesting the different scores such as upon allowance as revealed by Perkowsk ¶ [0025], ¶ [0358], ¶ [0178], ¶ [1239].
Claim 6 Crouse /Edmund/Perkowski/Zhang/Chan teaches all the limitations in claim 1 above.
Crouse further teaches “the operations further comprising”:
- “determining a market area associated with at least the subset of intellectual property assets of the plurality of intellectual property assets”; (Crouse ¶ [0067] 1st, 5th sentences: At 510, it is determined if the document is of specified document types. A user may specify both issued US patents and issued European patents in which case documents of either type would be determined to match the specified document type)
- “
- “calculating a subset weighted score for the subset of intellectual property assets that are associated with the market area by multiplying the individual subset breadth scores by a third weight based at least in part on a value of the respective subset breadth score for individual subset intellectual property assets that are associated with the market area” (Crouse ¶ [0067] 1st,5th sentences: At 510, it is determined if the document is of specified document types. A user specify both issued US patents and issued European patents in which case documents of either type would be determined to match the specified document type. Then at ¶ [0126] 1st-3rd, 11th sentences: At 622, overall scores are calculated from the word count ratios and commonness score ratios. The overall scores may be calculated by taking a square root of the sum of the square of the word count ratio (wcr) and square of commonness score ratio (csr) for the individual ones of the processed document portions. The relative weights of the word count ratio and commonness score may be normalized. Thus, the word count ratio may be weighted by first normalization value K (100/h-wcr) and the commonness score ratio may be weighted by a second normalization value L (100/h-csr) ); “and”
- “calculating a market breadth score for the subset of intellectual property assets that are associated with the market area by calculating an average of the individual subset weighted scores” (Crouse Fig.5 and ¶ [0067] 1st, 5th sentences: At 510, it is determined if the document is of one or more specified document types. A user may also specify both issued U.S. patents and issued European patents in which case documents of either type would be determined to match the specified document type.
Crouse Fig.7 ¶ [0084] 2nd sentence: At 702, documents are received. ¶ [0085] the pre-processing may use all or part of method 500 described in Fig.5 [such as at step 510 ¶ [0067] 1st, 5th sentences supra]. Then at step 718 of ¶ [0094], breadth scores for the document portions are calculated using the word count ratios and commonness score ratios. For instance, the breadth scores may be calculated by taking a square root of the sum of square of word count ratio (wcr) and square of commonness score ratio (csr) for individual ones of the processed document portions. In some instances, the relative weights of word count ratio and commonness score may be normalized. One technique for normalization is to set highest respective values for both word count ratio and commonness score ratio to 100. If highest word count ratio is h-wcr, then all of wcr for corpus will be multiplied by 100/h−wcr. Similar, normalization may be performed for commonness score ratio using highest commonness score ratio (h-csr). Of course, normalization values other than 100 may be used, 1000,500,50,10. Both are numbers, but the relative effect on a breadth score may not directly correspond to the respective numerical values. For example, word count ratio of 10 may have more or less impact on ultimate breadth than a commonness score ratio of 10. However, without normalization both contribute equally to the breadth score. As such, the word count ratio may be weighted by first normalization value K (100/h−wcr) and the commonness score ratio may be weighted by second normalization value L (100/h−csr). When written in an equation: Breadth Score=K(wcr2)+L(csr2). Thus, each document portion may be assigned its own breadth score. The breadth scores may be thought of as measuring the breadth of the document portions because the breadth scores are based on measures of word count and word commonness. This technique for determining a breadth score also moderates each of the underlying assumptions or premises behind the word count ratio and the commonness ratio. For example, if a patent claim is relatively shorter, but uses very uncommon terms, a patent practitioner might still consider the claim to be narrow due to the restrictive language in the claim. By defining a breadth score based on these two underlying assumptions, even shorter claims may be ranked not quite as broad if they use terms that are considered limiting or distinctive within a class in which ontology is well developed. Indeed per
Crouse ¶ [0029] 6th sentence: the overall breadth score may be represented by more than one score (e.g broadest breadth score, average, median, or mean breadth score, range of breadth scores) of the document portions or may be a composite (e.g. weighted) of such scores. Also per
Crouse ¶ [0030] 6th sentence: Where the overall breadth score is based on the score of multiple document portions (e.g., represented as an average, median, or mean; a weighted composite of the broadest, average (or median or mean), and narrowest or range score; or individual component scores such as broadest, average, and range), the calculation 118 compares that score or scores to the score or scores of the corresponding multiple document portions of other documents within the analysis).
* However *
Crouse does not teach: “calculating, for the subset of intellectual property assets that are associated with the market area, a subset breadth score based at least in part on a third word count score and a third commonness score for the respective portions of text included in the subset of intellectual property assets that are associated with the market area”; as claimed.
Edmund in analogous IP analysis teaches or suggests “the operations further comprising”:
- “calculating, for the subset of intellectual property assets that are associated with the market area, a subset breadth score based at least in part on a third word count score and a third commonness score for the respective portions of text included in the subset of intellectual property assets that are associated with the market area” (Edmund ¶ [0038] 6th , 8th sentences and ¶ [0005] 4th-7th sentences: For example, words in the preamble of claims for Chinese patent claims may be given weight for patent claim breadth, whereas the preamble of claims for patent applications in the United States may not be given weight for determining claim breadth. In some examples, patent claims in different types of jurisdiction patent applications may be analyzed in various ways. For example, to determine breath for patent claims in utility model patents filed in a jurisdiction, such as China, the utility model patent may have a scaling factor applied to it based on claim breadth scores determined for invention patents filed in that jurisdiction. Further discussion of these techniques for international claims are discussed below. Similarly, Fig.6 step 608 and Edmund ¶ [0119] 4th-5th sentences: Accordingly, the word count may not include words in the preamble of the claims for patents filed in the USA. Conversely, other jurisdictions (e.g., China) may give patentable weight to words in the preamble of claims, and thus the word count for claims filed in China may be included in the word count generated at 608. Then at ¶ [0123] 1st-4th sentences: At 616, a commonness score is generated for the processed document portions. Each document portion may be associated with its own commonness score. The commonness score is based on the frequency that the individual words in a particular document portion are found throughout the entire corpus of document portions under analysis. Thus, the commonness score for a document portion is based on the word frequencies of the words in that document portion, with the algorithm further detailed at ¶ [0123] 5th-6th sentences. Further as stated by
Edmund ¶ [0126] 1st,14th sentences: At 622, overall scores are calculated from the word count ratios and commonness score ratios. The overall scores may be thought of as measuring breadth of the document portions because the overall scores are based on measures of word count & word commonness)
Rationales to have modified/combined Crouse / Edmund are above and reincorporated.
Rationales to have modified/combined Crouse /Edmund/Perkowski/Zhang/Chan were also presented above.
Claim 7 Crouse /Edmund/Perkowski/Zhang/Chan teaches all limitations in claim 6 above.
Crouse does not teach “wherein the market breadth score comprises a first market breadth score associated with the first period of time period and the operations further comprising”:
- “determining at least a second market breadth score for the plurality of intellectual property assets associated with the second period of time, wherein the second period of time is different from the first period of time”;
- “displaying to the user, the first market breadth score and the second market breadth score”.
Edmund however in analogous analysis of intellectual property teaches/suggests:
- “determining at least a second market breadth score for the plurality of intellectual property assets associated with the second period of time, wherein the second period of time is different from the first period of time”; (Edmund Fig. 7 step 702 and ¶ [0134] At 702, a data file is obtained. In an implementation, the data file may be obtained from one of the data repositories 102 show in Fig.1. For example, per ¶ [0031], ¶ [0033] The available data repositories may include, a patent database provided and/or supported by a patent office of a particular country (e.g USPTO (United States Patent and Trademark Office) database, a PAIR (Patent Application Information Retrieval) database, EPO (European Patent Office) database, WIPO (World Intellectual Property Organization) database, SIPO (State Intellectual Property Office of P.R.C.) database, etc.). Similarly, ¶ [0038] 6th sentence: user specify issued US patents and issued European patents in which case documents of either type would be determined to match specified document type. Next, Fig.7 and ¶ [0149] 1st-3rd sentences: one or more of the steps of method 700 described from 702-724 may be performed for different time in prosecution of a patent or corpus of patent documents. For example a claim breadth score may be determined for patents in a corpus at 1st point in time such as when patents were filed or before amendments were made to the claims (or any other point in prosecution). Additionally, the claim breadth scores may be determined for the patents at 2nd point in time, such as a point in time corresponding to when the claims were allowed (or any other time in prosecution) “and”
- “displaying to the user, the first market breadth score and the second market breadth score”
(Edmund Fig.7 and ¶ [0149] 1st-3rd sentences: the steps of method 700 described from 702-724 may be performed for different time in prosecution of a patent or a corpus of patent documents. For example, a claim breadth score may be determined for patents in a corpus at a first point in time, such as when the patents were filed, or before amendments were made to the claims (or any other point in prosecution). Additionally, the claim breadth scores may be determined for the patents at a second point in time, such as a point in time corresponding to when the claims were allowed (or any other time in prosecution). ¶ [0038] 6th sentence: user specify issued U.S. patents and issued European patents in which case documents of either type would be determined to match specified document type
Edmund Fig.7 step 724: generate UI displaying the ranking).
Rationales to have modified/combined Crouse/Edmund are above and reincorporated.
Rationales to have modified/combined Crouse /Edmund/Perkowski/Zhang/Chan were also presented above.
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Claims 8, 9 are rejected under 35 U.S.C. 103 as being unpatentable over:
Crouse /Edmund/Perkowski/Zhang/Chan as applied to claim 1 above, in view of
Jonathan A. Barney US 20160004768 A1 hereinafter Barney, and in further view of
Lee; Lewis C. et al, US 20140279584 A1 hereinafter Lee. As per,
Claim 8 Crouse /Edmund/Perkowski/Zhang/Chan teaches all limitations in claim 1. Further,
Crouse teaches: “the entity comprises a first entity, the operations further comprising”
(Crouse ¶ [0137] 4th sentence: The collection of patents or patent applications may be defined by, a portfolio of a patent owner. ¶ [0023] 5th sentences: Data filtering 104 may also filter documents by author, by inventor, assignee, technical field, classification etc. ¶ [0062] 2nd-3rd sentences: Each document in the data repository may be associated with a unique document identification number. a patent document which may include application, publication, patent number, and/or combination of info associated with the patent document that uniquely identify the patent document (such as combination of name of inventor, filing date):
- “determining a technology area associated with at least a first subset of the plurality of intellectual property assets” (Crouse ¶ [0061] 2nd,4th sentences: collection of patents and/or applications gathered from data repository limited to technology area. the collection obtained based on classification codes, such as USPTO classes, subclasses, IPC. ¶ [0024] 14th sentence data filtering 104 restricts the documents to specific technical area. ¶ [0031] the design for analysis captures the idea of comparing apples to apples when calculating comprehensive breadth scores. For instance, comparison of the breadth of a biotechnology patent to the breadth of a mechanical patent is less meaningful than comparing the breadth of one software patent to the breadth another software patent. Because the documents are given overall breadth scores with respect to other documents in same corpus, those overall breadth scores may be utilized to determine the comprehensive breadth scores for each of the documents);
Crouse /Edmund/Perkowski/Zhang/Chan does not teach:
- “identifying a second entity associated with the technology area”;
- “identifying a second subset of intellectual property assets that are associated with the second entity”;
- “calculating, for the second subset of intellectual property assets that are associated with the second entity, a subset breadth score based at least in part on a third word count score and a third commonness score for the respective portions of text included in the second subset of intellectual property assets that are associated with the second entity”; as claimed in combination.
* However *
Barney in analogous analysis of intellectual property teaches/suggests:
- “identifying a second entity associated with the technology area”; (Barney ¶ [0051] Fig.10B shows a drill-down view of aerospace technology space of Fig. 10A wherein patents owned by selected competitors have been highlighted and color/shape coded. ¶ [0165] 5th sentence: In this case patents owned by selected competitors have been highlighted and color/shape coded. ¶ [0172] 4th sentence: Relevance scores could also be generated relative to identified competitors to determine and measure how a particular acquisition target might look strategically to other major players in a technology space. ¶ [0222] 3rd-5th sentences: For example, two competitors may enter into a cross license agreement whereby each competitor is provided with a non-exclusive license under the other's patent portfolio. Typically, the exchange of licensed rights may not be identically balanced. For example, one competitor may have more extensive patent coverage than the other in a relevant technology space; and/or one competitor may have patents covering higher-value products and/or products produced at higher profit margins than the other)
- “identifying a second subset of intellectual property assets that are associated with the second entity”; (Barney ¶ [0165] 5th-8th sentences: In this case patents owned by selected competitors have been highlighted and color/shape coded. This drill-down view may be particularly useful for purposes of strategic planning, strategic acquisition analysis, and industry economic/financial analysis. Highlighting patents owned by selected competitors facilitates better and more strategic understanding the competitive landscape in a target technology space. It also quickly and visually communicate which competitors own or dominate certain concentrations of patents and the how the various concentrations or clusters interrelate)
- “calculating, for the second subset of intellectual property assets that are associated with the second entity, a subset breadth score based at least in part on a third word count score and a third commonness score for the respective portions of text included in the second subset of intellectual property assets that are associated with the second entity; (Barney ¶ [0215] 4th-6th sentences: Using relative breadth as the dependent regression variable one can construct and optimize a regression algorithm that would be predictive of relative claim breadth. Independent predictor variables could include for example: claim word count, unique word count, particular word and word combination frequencies, limiting or restricting words, broadening or inclusive words, semantic similarity scores between two or more claims, number of relevant documents and associated relevance scores, and the like. Those skilled in the art will recognize that the regression analysis can be formulated and optimized to predict whether an analyzed claim is likely broader or narrower than one or more comparison or reference claims. For example, see
Barney ¶ [0145] Another particularly preferred technique for measuring contextual relatedness or contextual similarity between two or more documents P1, P2 is to: i) identify a list of words used in each document along with calculated word frequencies (number of times each word is used divided by the total word count for each document); ii) multiply each corresponding word frequency to obtain a frequency product for each word; iii) divide each frequency product by one-half the sum of the squares of each corresponding word frequency; and iv) take the sum total of the result for each word. In formulaic terms this may be expressed as:
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It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have clarified or at most have complimentarily modified Crouse /Edmund/Perkowski/Zhang/Chan system to have further included Barney’s teachings/ suggestion to have improved the IP search algorithm, database and user interface to overcome the preexisting deficiencies on handling a wide variety of searching applications and to have also mitigated the deficiencies of preexisting methods and systems in displaying and communicating input/output search criteria and search results in a way that would have facilitated intuitive understanding and visualization of the logical relationships sought to have been explored between two or more related concepts being searched (Barney ¶ [0012] in view of MPEP 2143 G).
The predictability of such modification would have been further corroborated by the broad level of skills of one of ordinary skills in the art as articulated by Crouse ¶ [0163] in view of Edmund at ¶ [0183] in view of Perkowski ¶¶ [0009] 3rd sentence, [0132], [1539], [1638], [1754], Zhang ¶ [0014], ¶ [0068], and Chan at ¶ [0511]-¶ [0512] and, in further view of Barney at ¶ [0241].
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with quantifying and visualizing intellectual property metrics. In such combination each element would have merely performed same analytical function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements per Crouse ¶ [0163] in view of Edmund at ¶ [0183] in view of Perkowski ¶¶ [0009] 3rd sentence, [0132], [1539], [1638], [1754], Chan at ¶ [0511]-¶ [0512] and in further view of Barney at ¶ [0241], the to be combined elements would have fitted together like pieces of puzzle in logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of combination would have been predictable (MPEP 2143 A).
* Further still *
Crouse/Edmund/Perkowski/ Zhang/Chan/Barney in combination still does not teach:
- “calculating a subset weighted score for the second subset of intellectual property assets that are associated with the second entity by multiplying the individual subset breadth scores by a third weight based at least in part on a value of a respective subset breadth score for individual second subset intellectual property assets that are associated with the second entity”;
- “calculating a subset comprehensive breadth score for the second subset of intellectual property assets that are associated with the second entity by calculating an average of the individual subset weighted scores” as explicitly claimed.
* However *
Lee in analogous analysis of intellectual property teaches/suggests:
- “calculating a subset weighted score for the second subset of intellectual property assets that are associated with the second entity by multiplying the individual subset breadth scores by a third weight based at least in part on a value of a respective subset breadth score for individual second subset intellectual property assets that are associated with the second entity”;
(Lee [0065] 1st-5th sentences: x-value may be a function of the collection-based frequency counts associated with each of the words in the claim. One particular implementation employs algorithm of one divided by sum of inverse of each word's associated frequency count (1/sum (1/Freq wd1+1/Freq wd2+…+1/Freq wd n), where Freq wd1 is the count of a number of occurrences of unique word 1 in the claims from the collection of patents/applications. Less common terms result in larger denominator values (1/low_freq_value>1/high_freq_value) thus making the overall result smaller. A larger x-value is thus assigned to claims that use relatively more common words for the collection of patents/applications being evaluated and smaller x-value is assigned to claims that use less common words. In this manner, this 2nd value or coordinate represents an underlying assumption or premise that claims with more common words tend to be broader than claims with less common words)
- “calculating a subset comprehensive breadth score for the second subset of intellectual property assets that are associated with the second entity by calculating an average of the individual subset weighted scores” (Lee ¶ [0066] With the x and y-value each claim can then be plotted in 2D graph which visually reveals how a particular claim compares in terms of word count and commonness to all of the other claims in the collection of patents being reviewed. Thus, for a given patent having M claims, the plot may show M designators or marks in a two-dimensional area. The location of the designators or marks indicates whether the claims are relatively broader or narrower within the collection. Claims with x- and y-values closer to the origin (i.e. claim has many words and the words contain uncommon words) are said to be narrower than claims farther from the origin (i.e., claims with fewer words and the words are more common.
Lee ¶ [0068] With the x- and y-values, the claim scope engine also compute a distance value from origin. In this manner, each claim in a patent or patent application may have a unique distance value based on these 2 values or coordinates. The distance value may then be used to rank or otherwise order any results from search engine 122 and analysis tools or analysis module 124. Further, the distance value may be used to alter visual appearances in various graphical outputs, to convey to user which assets in a given view may be broader than others. For instance, in a portfolio view or concept scatter plot, the distance value may be employed to alter sizes, color intensities or color frequencies of designators or marks in results shown in the portfolio view or concept scatter plot to visually convey relative quality or breadth of corresponding claims in patents and/or patent applications. ¶ [0130] last 3 sentences: graphical chart 1102 includes line 1104 that represents average scope score for patent documents within a portfolio of a respective owner. point 1106 on line 1104 shows company E includes a relatively high average patent scope score for patent documents in company E's patent portfolio. The graphical chart 1102 also includes a line 1108 that represents an average relevance score of the portfolio).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have further modified Crouse/Edmund/Perkowski/Zhang/Chan/Barney’s “system” to have included Lee’s teachings or suggestions in order to have conveyed to the user a more comprehensive visual appearance relative quality or breadth of corresponding claims in patents and/or patent applications (Lee ¶ [0068] last sentence in view of MPEP 2143 G). The predictability of such modification would have been further corroborated by the broad level of skills of one of ordinary skills in the art as articulated by Crouse ¶ [0163] in view of Edmund at ¶ [0183] in view of Perkowski ¶ [0009] 3rd sentence, ¶ [0132], [1539], [1638], [1754], Zhang ¶ [0014], [0068]. Chan ¶ [0511]-¶ [0512] in view of Barney at ¶ [0241], in further view of Lee at ¶ [0157].
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar analysis of intellectual property field of endeavor. In such combination each element would have merely performed same analytical function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced Crouse/Edmund/Perkowski/Zhang/Chan/ Barney in view of Lee, the to be combined elements would have fitted together like puzzle pieces in logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Claim 9 Crouse/Edmund/Perkowski/ Zhang/Chan/Barney/Lee teaches all limitations in claim 8.
Crouse does not teach: “wherein the subset comprehensive breadth score for the second subset of intellectual property assets associated with the second entity comprises a first subset comprehensive breadth score associated with the first period of time and the operations further comprising”:
- “determining at least a second subset comprehensive breadth score for the second subset of intellectual property assets associated with the second entity”; “and”
- “displaying, to the user, the first subset comprehensive breadth score and the second subset comprehensive breadth score” as claimed.
* However *
Edmund in analogous IP analysis teaches/suggests: “wherein the subset comprehensive breadth score for the second subset of intellectual property assets associated with the second entity comprises a first subset comprehensive breadth score associated with the first period of time” (Edmund Fig.7, ¶ [0149] 2nd sentence: a claim breadth score may be determined for patents in a corpus at 1st point in time, when patents were filed, or before amendments were made to the claims) “and the operations further comprising”:
- “determining at least a second subset comprehensive breadth score for the second subset of intellectual property assets associated with the second entity”; (Edmund Fig.7, ¶ [0145] 3rd - 4th sentences: The relative impact of the word count score and of the commonness score may be modified by weighting the raw score values to create weighted scores. This may be repeated for each patent claim under analysis so that each patent claim is now associated with a new piece of data representing an associated claim breadth score. ¶ [0149] 1st-3rd sentences: steps 702-724 may be performed for different time in prosecution of a patent or a corpus of patent documents. For example, a claim breadth score may be determined for patents in a corpus at 1st point in time, when patents were filed, or before amendments were made to the claims (or any point in prosecution). Additionally, the claim breadth scores may be determined for the patents at a 2nd point in time such as a point in time corresponding to when claims were allowed (or any other time in prosecution) “and”
- “displaying, to the user the first subset comprehensive breadth score and the second subset comprehensive breadth score” (Edmund Fig.7 step 724: generate a UI displaying the ranking in view of ¶ [0149] 2nd-3rd sentences);
Rationales to have modified/combined Crouse / Edmund are above and reincorporated.
Rationales to have modified/combined Crouse / Edmund / Perkowski / Zhang / Chan / Barney/Lee were also presented above.
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Conclusion
The following art is made of record and considered pertinent to Applicant's disclosure:
WO 2016157214 A1 teaching Intellectual property management system and tool
WO 2021003187 A1 teaching Analysis of intellectual-property data in relation to products and services
WO 2020072033 A1 teaching Frameworks for the analysis of intangible assets
Aon New Quality of Intellectual Property Solution Helps Companies Realize Full Value of their IP Portfolio, aon mediaroom webpages, Nov 10, 2020
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Insurance Risk Study, Fourteenth Edition, Global Risk, Profitability, and Growth Metrics, aon webpages, aon plc 2019
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Intellectual Property Solutions for M&A and Market Transactions, aon webpages, aon plc 2019
Realize IP Alpha, Aon Quality of Intellectual Property Report, Aon webpages, Aon plc 2020
Unlock the Value of Intellectual Property, aon webpages, aon plc 2020
Bringing additional value to your firm, aon webpages, December 10th, 2020
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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 OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Octavian Rotaru/
Primary Examiner, Art Unit 3624
March 4th, 2026
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7 MPEP 2111.04 I ‘whereby clause in a method claim is not given weight when it simply expresses the intended result of a process step positively recited.’" Id. (quoting Minton v. Nat’l Ass’n of Securities Dealers, Inc., 336 F.3d 1373, 1381, 67 USPQ2d 1614, 1620 (Fed. Cir. 2003))
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9 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;
10 Apple, Inc. v. Ameranth, Inc., 842 F.3d 1229, 1244, 120 USPQ2d 1844, 1856 (Fed. Cir. 2016);
11 Flook, 437 U.S. at 594, 198 USPQ2d at 199; Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)
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