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How Microsoft anticipated wearables, machine learning and cognitive enhancement: Key Neurotech Patent #18


– Illus­tra­tive image from U.S. Patent No. 6,842,877

Let’s dis­cuss today a fas­ci­nat­ing patent assigned to Microsoft back in 2015. (As men­tioned, we are fea­tur­ing a foun­da­tion­al Per­va­sive Neu­rotech patent a day, from old­er to new­er by issue date)

U.S. Patent No. 6,842,877: Con­tex­tu­al respons­es based on auto­mat­ed learn­ing tech­niques.

  • Assignee(s): Microsoft Cor­po­ra­tion
  • Inventor(s): James O. Robarts Eric L. Mat­te­son
  • Tech­nol­o­gy Cat­e­go­ry: Neu­rocog­ni­tive Train­ing
  • Issue Date: Jan­u­ary 11, 2005

SharpBrains’ Take:

The ‘877 patent dis­clos­es wear­able devices that incor­po­rate tech­niques for user-feed­back loops to auto­mat­i­cal­ly improve a sys­tem’s response, for exam­ple when the com­put­ing sys­tem iden­ti­fies  user’s needs and pref­er­ences through the use of sens­ing com­po­nents, in a vari­ety of envi­ron­men­tal con­texts.

Cog­ni­tive ben­e­fits may occur, when the auto­mat­ed response results in pro­vid­ing a tem­plate for user-improve­ment, such as using a per­son­’s psy­cho­log­i­cal pro­file under the Mey­er-Brig­gs test to train an indi­vid­ual to be more out­spo­ken if they are an intro­vert or be aware that, as an extro­vert, they are spend­ing too much time talk­ing. Although the claims are focused on the adap­tive learn­ing aspect of the wear­able device, this patent is a key non-inva­sive neu­rotech­nol­o­gy patent due to the inter­sec­tion of the fast-grow­ing wear­able tech sec­tor with the cog­ni­tive enhance­ment appli­ca­tions dis­closed there­in, the lengthy and detailed spec­i­fi­ca­tion (with 48 illus­tra­tion sheets and 31 pages of writ­ten mate­r­i­al), and the appli­ca­tion hav­ing received an aver­age of 43 cita­tions per year.


Tech­niques are dis­closed for using a com­bi­na­tion of explic­it and implic­it user con­text mod­el­ing tech­niques to iden­ti­fy and pro­vide appro­pri­ate com­put­er actions based on a cur­rent con­text, and to con­tin­u­ous­ly improve the pro­vid­ing of such com­put­er actions. The appro­pri­ate com­put­er actions include pre­sen­ta­tion of appro­pri­ate con­tent and func­tion­al­i­ty. Feed­back paths can be used to assist auto­mat­ed machine learn­ing in detect­ing pat­terns and gen­er­at­ing inferred rules, and improve­ments from the gen­er­at­ed rules can be imple­ment­ed with or with­out direct user con­trol. The tech­niques can be used to enhance soft­ware and device func­tion­al­i­ty, includ­ing self-cus­tomiz­ing of a mod­el of the user’s cur­rent con­text or sit­u­a­tion, cus­tomiz­ing received themes, pre­dict­ing appro­pri­ate con­tent for pre­sen­ta­tion or retrieval, self-cus­tomiz­ing of soft­ware user inter­faces, sim­pli­fy­ing repet­i­tive tasks or sit­u­a­tions, and men­tor­ing of the user to pro­mote desired change.

Illus­tra­tive Claim 41. A wear­able com­put­ing sys­tem con­fig­ured to improve auto­mat­ed respons­es to a cur­rent con­text for a user, the cur­rent con­text being rep­re­sent­ed by a plu­ral­i­ty of con­text attrib­ut­es that each mod­el an aspect of the con­text, mul­ti­ple defined con­tex­tu­al sit­u­a­tions each spec­i­fy­ing val­ues for at least one of the con­text attrib­ut­es, mul­ti­ple auto­mat­ed respons­es being asso­ci­at­ed with the defined con­tex­tu­al sit­u­a­tions, com­pris­ing:

  • a first com­po­nent that is con­fig­ured to repeat­ed­ly, receive an indi­ca­tion of cur­rent con­text infor­ma­tion that includes cur­rent val­ues for each of at least some of the plu­ral­i­ty of con­text attrib­ut­es, deter­mine one of the defined con­tex­tu­al sit­u­a­tions that match­es the indi­cat­ed cur­rent con­text infor­ma­tion, deter­mine one of the auto­mat­ed respons­es that is asso­ci­at­ed with the one defined con­tex­tu­al sit­u­a­tion, receive an indi­ca­tion from the user of an alter­nate auto­mat­ed response, and store an indi­ca­tion of the indi­cat­ed cur­rent con­text infor­ma­tion and the alter­nate auto­mat­ed response; and
  • a sec­ond com­po­nent that is con­fig­ured to auto­mat­i­cal­ly detect a rela­tion­ship between an iden­ti­fied con­tex­tu­al sit­u­a­tion and one of the alter­nate auto­mat­ed respons­es based on that alter­nate auto­mat­ed response being pre­vi­ous­ly indi­cat­ed by the user and to cre­ate an asso­ci­a­tion between the iden­ti­fied con­tex­tu­al sit­u­a­tion and the one alter­nate auto­mat­ed response so that the one alter­nate auto­mat­ed response can in the future be pro­vid­ed to the user for that con­tex­tu­al sit­u­a­tion.

To learn more about mar­ket data, trends and lead­ing com­pa­nies in the dig­i­tal brain health space –dig­i­tal plat­forms for brain/ cog­ni­tive assess­ment, mon­i­tor­ing and enhance­ment– check out this mar­ket report. To learn more about our analy­sis of 10,000+ patent fil­ings, check out this IP & inno­va­tion neu­rotech report.

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As seen in The New York Times, The Wall Street Journal, BBC News, CNN, Reuters,  SharpBrains is an independent market research firm tracking how brain science can improve our health and our lives.

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