DETAILED ACTION
Summary and Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office Action is in response to Applicant’s reply filed 1/12/2026.
Claims 2 and 3 are new.
Claims 1-3 are pending.
Claims 1 and 2 are rejected under 35 U.S.C. 103 as being unpatentable over Rajun et al. (US Patent Pub 2006/0212413), in view of Encina et al. (US Patent Pub 2008/0140616).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Rajun et al. (US Patent Pub 2006/0212413), in view of Encina et al. (US Patent Pub 2008/0140616), further in view of Fagerholm et al. (US Patent 2003/0144883).
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Information Disclosure Statement
The information disclosure statement filed 2/27/2026 has been fully considered, initialed, and signed by the Examiner. A copy is attached to this Office action.
Note on Prior Art Rejections
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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 2 are rejected under 35 U.S.C. 103 as being unpatentable over Rajun et al. (US Patent Pub 2006/0212413) (Rajun), in view of Encina et al. (US Patent Pub 2008/0140616) (Encina).
In regards to claim 1, Rajun discloses a computer-implemented method for identifying a level of similarity between a user-provided data item and a data item within a set of data documents, the method comprising:
clustering, by a reference map generator executing on a first computing device, in a two-dimensional metric space, a set of data documents selected according to at least one criterion, generating a semantic map (Rajun at paras. 0008-9, 0011-13)1;
associating, using the semantic map, a coordinate pair with each data document in the set of data documents (Rajun at paras. 0052, 0057)2;
generating, by a parser executing on the first computing device, an enumeration of terms occurring in the set of data documents (Rajun at para. 0060)3;
determining, by a representation generator executing on the first computing device, for each term in the enumeration, occurrence information including: (i) a number of data documents in which the term occurs, (ii) a number of occurrences of the term in each data document, and (iii) the coordinate pair associated with each data document in which the term occurs (Rajun at paras. 0060-61)4;
generating, by the representation generator, for each term in the enumeration, a sparse distributed representation (SDR) using the occurrence information (Rajun at paras. 0012, 0060-61)5;
storing, in an SDR database, each of the generated SDRs (Rajun at para. 0102)6;
generating, by the representation generator, at least one SDR for the plurality of terms (Rajun at paras. 0012, 0060-61)7;
Rajun does not expressly disclose (1) the terms are measurements and the documents are associated with a medical diagnosis, (2) receiving, by a diagnosis support module executing on a second computing device and in communication with the first computing device, from a third computing device, a document comprising a plurality of measurements, the document associated with a medical patient, (3) generating, by the representation generator, a compound SDR for the document, based on the at least one SDR generated for the plurality of measurements, (4) determining, by a similarity engine executing on the second computing device, a level of semantic similarity between the compound SDR generated for the document and an SDR retrieved from the SDR database, and (5) providing, by the diagnosis support module, to the third computing device, an identification of the medical diagnosis associated with the SDR retrieved from the SDR database, based on the determined level of semantic similarity.
Encina discloses a system and method for document processing to enable topic based searching using an input query. Documents can be used to provide a diagnosis (i.e., documents are associated with a medical diagnosis). Encina at para. 0109. An input query can comprise a complex text in the form of a paragraph (i.e., document) and includes information about a patient (i.e., receiving … a document associated with a medical patient) and can include content relating to symptoms (i.e., measurements). Encina at Fig. 20; paras. 0056-57, 0164. The query is analyzed to generate fingerprints based on identified topics (i.e., measurements), wherein the fingerprints are binary vectors (i.e., generating … a compound SDR for the document based on the at least one SDR). Encina at paras. 0056-57. The query fingerprint (i.e., compound SDR) is searched against the index of document fingerprints based on similarity score (i.e., determining … a level of similarity between the compound SDR and the SDR from the SDR database). Encina at para. 0056. The results are displayed based on the similarity, where the result can provide an identification of a medical diagnosis associated with the resulting SDRs (i.e., providing … an identification of the medical diagnosis …). Encina at para. 0056, 0164, “Table 15” – “system provides diagnosis of diseases that most fit the issued query”.
Rajun and Encina are analogous art because they are directed to the same field of endeavor of document querying.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Rajun by adding the features of (1) the terms are measurements and the documents are associated with a medical diagnosis, (2) receiving, by a diagnosis support module executing on a second computing device and in communication with the first computing device, from a third computing device, a document comprising a plurality of measurements, the document associated with a medical patient, (3) generating, by the representation generator, a compound SDR for the document, based on the at least one SDR generated for the plurality of measurements, (4) determining, by a similarity engine executing on the second computing device, a level of semantic similarity between the compound SDR generated for the document and an SDR retrieved from the SDR database, and (5) providing, by the diagnosis support module, to the third computing device, an identification of the medical diagnosis associated with the SDR retrieved from the SDR database, based on the determined level of semantic similarity, as disclosed by Encina.
The motivation for doing so would have been because converting information in documents to canonicalized representations reduces false negatives in search results. Encina at para. 0063.
In regards to claim 2, Rajun in view of Encina discloses the method of claim 1, wherein clustering, by the reference map generator executing on the first computing device, in the two-dimensional metric space, the set of data documents selected according to at least one criterion and associated with a medical diagnosis, generating a semantic map, wherein at least one of the set of data documents includes metadata. Encina at para. 0062.8
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Rajun et al. (US Patent Pub 2006/0212413) (Rajun), in view of Encina et al. (US Patent Pub 2008/0140616) (Encina), further in view of Fagerholm et al. (US Patent 2003/0144883) (Fagerholm).
In regards to claim 3, Rajun in view of Encina discloses the method of claim 2 but does not expressly disclose further comprising, before providing the identification, applying, by the diagnosis support module, a rule to the identification of the medical diagnosis and to the metadata to provide confirmation of a level of accuracy of the identification of the medical diagnosis.
Fagerholm discloses a method for analyzing side effects and interactions of pharmaceuticals. The method utilizes a medical database as a guideline (i.e., rule) to determine whether a patient’s diagnosis and treatment have any issues. The method considers individual characteristics of the patient in doing so (i.e., metadata). Fagerholm at paras. 0020-21.
Rajun, Encina, and Fagerholm are analogous art because they are directed to the same field of endeavor of searching based on provided information.
At the time before the effective filing date of the instant application, it would have been obvious to one of ordinary skill in the art to modify Rajun in view of Encina by adding the features of before providing the identification, applying, by the diagnosis support module, a rule to the identification of the medical diagnosis and to the metadata to provide confirmation of a level of accuracy of the identification of the medical diagnosis, as disclosed by Fagerholm.
The motivation for doing so would have been to ensure that there are no issues with the provided diagnosis that could potentially cause problems. Fagerholm at para. 0021.
Response to Amendment
Specification
Applicant’s amendment to the specification is acknowledged. Consequently, objection to the specification is withdrawn.
Objection to claim 1 for Minor Informalities
Applicant’s amendment to claim 1 to address the minor informalities is acknowledged. Consequently, the objection to claim 1 is withdrawn.
Rejection of Claim 1 under 35 U.S.C 112(b)
Applicant’s amendment to claim 1 is acknowledged. Consequently, rejection to claim 1 under 35 U.S.C. 112(b) is withdrawn.
Double patenting rejection
The terminal disclaimer filed and approved on 1/12/2026 is acknowledged. Consequently, the rejection to claim 1 for nonstatutory double patenting is withdrawn.
Additional Prior Art
Additional relevant prior art are listed on the attached PTO-892 form. Some examples are:
Kawatani (US Patent Pub 2003/0028558) discloses a system and method for extracting terms, phrases, and sentences and generating vectors to represent them.
Matsubayashi et al. (US Patent Pub 2003/0065658) discloses a system and method for searching similar documents using generated characteristic vectors.
Palmer et al. (US Patent 6,990,628) discloses a system and method for measuring similarity among documents using vectors.
Takacs et al. (US Patent Pub 2011/0286627) discloses a system and method for recognizing feature descriptors. The method includes querying vectors based on similarity value, where the vectors are based on term frequency.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Michael Le whose telephone number is 571-272-7970 and fax number is 571-273-7970. The examiner can normally be reached Mon-Fri 9:30 AM – 6 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Tony Mahmoudi can be reached on 571-272-4078. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL LE/Examiner, Art Unit 2163
/ALEX GOFMAN/Primary Examiner, Art Unit 2163
1 A vector space is created (i.e., generating a semantic map … in a two dimensional metric space) using documents. The documents are clustered into class spaces based on similar content (i.e., selected according to at least one criterion).
2 Documents are represented as vectors in the vector space (i.e., 2D semantic map). A point, which represents the end tip of the vector corresponds to a coordinate pair.
3 A dictionary of terms of all the documents is generated.
4 Term frequency in documents and among the documents are determined. Documents are mapped with the determined data (i.e., determining … the coordinate pair associated with each data document…).
5 Sparse representations are generated for the documents based on terms and their occurrence information.
6 The document database system stores documents and associated sparse document vector (i.e., SDR), which is used to index the documents for searching and document retrieval.
7 Sparse representations are generated for the documents based on terms in a document.
8 Documents contain meta-tags (i.e., metadata).
Prosecution Insights